{"id":4040,"date":"2025-10-11T21:08:36","date_gmt":"2025-10-11T13:08:36","guid":{"rendered":"http:\/\/viplao.com\/?p=4040"},"modified":"2025-10-11T21:12:30","modified_gmt":"2025-10-11T13:12:30","slug":"%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e5%ae%a2%e6%88%b7%e6%9c%8d%e5%8a%a1%e8%b4%a8%e9%87%8f%e6%8f%90%e5%8d%87%e4%b8%8e%e6%bb%a1%e6%84%8f%e5%ba%a6%e8%b0%83%e6%9f%a5%e5%ae%9e","status":"publish","type":"post","link":"http:\/\/viplao.com\/index.php\/2025\/10\/11\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e5%ae%a2%e6%88%b7%e6%9c%8d%e5%8a%a1%e8%b4%a8%e9%87%8f%e6%8f%90%e5%8d%87%e4%b8%8e%e6%bb%a1%e6%84%8f%e5%ba%a6%e8%b0%83%e6%9f%a5%e5%ae%9e\/","title":{"rendered":"\u3010Python\u5b9e\u8df5\u6848\u4f8b\u3011\u5ba2\u6237\u670d\u52a1\u8d28\u91cf\u63d0\u5347\u4e0e\u6ee1\u610f\u5ea6\u8c03\u67e5\u5b9e\u8df5"},"content":{"rendered":"\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_71 counter-hierarchy ez-toc-counter ez-toc-grey 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href=\"http:\/\/viplao.com\/index.php\/2025\/10\/11\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e5%ae%a2%e6%88%b7%e6%9c%8d%e5%8a%a1%e8%b4%a8%e9%87%8f%e6%8f%90%e5%8d%87%e4%b8%8e%e6%bb%a1%e6%84%8f%e5%ba%a6%e8%b0%83%e6%9f%a5%e5%ae%9e\/#%E4%BA%8C%E3%80%81%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86NLP%E5%BA%94%E7%94%A8\" title=\"\u4e8c\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406(NLP)\u5e94\u7528\">\u4e8c\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406(NLP)\u5e94\u7528<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/11\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e5%ae%a2%e6%88%b7%e6%9c%8d%e5%8a%a1%e8%b4%a8%e9%87%8f%e6%8f%90%e5%8d%87%e4%b8%8e%e6%bb%a1%e6%84%8f%e5%ba%a6%e8%b0%83%e6%9f%a5%e5%ae%9e\/#%E4%B8%89%E3%80%81%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90%E5%B8%AE%E5%8A%A9%E7%90%86%E8%A7%A3%E9%A1%BE%E5%AE%A2%E6%83%85%E7%BB%AA\" title=\"\u4e09\u3001\u60c5\u611f\u5206\u6790\u5e2e\u52a9\u7406\u89e3\u987e\u5ba2\u60c5\u7eea\">\u4e09\u3001\u60c5\u611f\u5206\u6790\u5e2e\u52a9\u7406\u89e3\u987e\u5ba2\u60c5\u7eea<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/11\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e5%ae%a2%e6%88%b7%e6%9c%8d%e5%8a%a1%e8%b4%a8%e9%87%8f%e6%8f%90%e5%8d%87%e4%b8%8e%e6%bb%a1%e6%84%8f%e5%ba%a6%e8%b0%83%e6%9f%a5%e5%ae%9e\/#%E5%9B%9B%E3%80%81%E5%AE%9E%E6%88%98%E6%A1%88%E4%BE%8B%EF%BC%9A%E5%88%9B%E5%BB%BA%E4%B8%80%E4%B8%AA%E8%81%8A%E5%A4%A9%E6%9C%BA%E5%99%A8%E4%BA%BA%E5%8E%9F%E5%9E%8B\" title=\"\u56db\u3001\u5b9e\u6218\u6848\u4f8b\uff1a\u521b\u5efa\u4e00\u4e2a\u804a\u5929\u673a\u5668\u4eba\u539f\u578b\">\u56db\u3001\u5b9e\u6218\u6848\u4f8b\uff1a\u521b\u5efa\u4e00\u4e2a\u804a\u5929\u673a\u5668\u4eba\u539f\u578b<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"http:\/\/viplao.com\/index.php\/2025\/10\/11\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e5%ae%a2%e6%88%b7%e6%9c%8d%e5%8a%a1%e8%b4%a8%e9%87%8f%e6%8f%90%e5%8d%87%e4%b8%8e%e6%bb%a1%e6%84%8f%e5%ba%a6%e8%b0%83%e6%9f%a5%e5%ae%9e\/#Python%E5%AE%9E%E8%B7%B5%E6%A1%88%E4%BE%8B\" title=\"Python\u5b9e\u8df5\u6848\u4f8b\">Python\u5b9e\u8df5\u6848\u4f8b<\/a><\/li><\/ul><\/nav><\/div>\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E5%AE%A2%E6%88%B7%E6%9C%8D%E5%8A%A1%E8%B4%A8%E9%87%8F%E6%8F%90%E5%8D%87%E4%B8%8E%E6%BB%A1%E6%84%8F%E5%BA%A6%E8%B0%83%E6%9F%A5\"><\/span><strong>\u5ba2\u6237\u670d\u52a1\u8d28\u91cf\u63d0\u5347\u4e0e\u6ee1\u610f\u5ea6\u8c03\u67e5<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E4%B8%80%E3%80%81%E5%AE%A2%E6%88%B7%E6%9C%8D%E5%8A%A1%E6%94%B9%E8%BF%9B%E7%82%B9%E8%AF%86%E5%88%AB\"><\/span><strong>\u4e00\u3001\u5ba2\u6237\u670d\u52a1\u6539\u8fdb\u70b9\u8bc6\u522b<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ol>\n<li><strong>\u5ba2\u6237\u670d\u52a1\u8bb0\u5f55\u5206\u6790\u7684\u91cd\u8981\u6027<\/strong>\n<ul>\n<li><strong>\u5b9a\u4e49<\/strong>\uff1a\u5ba2\u6237\u670d\u52a1\u8bb0\u5f55\u5305\u62ec\u5ba2\u6237\u54a8\u8be2\u3001\u6295\u8bc9\u3001\u5efa\u8bae\u3001\u53cd\u9988\u7b49\u4fe1\u606f\uff0c\u662f\u4e86\u89e3\u5ba2\u6237\u9700\u6c42\u548c\u95ee\u9898\u7684\u5b9d\u8d35\u8d44\u6e90\u3002<\/li>\n\n\n\n<li><strong>\u91cd\u8981\u6027<\/strong>\uff1a\n<ul>\n<li><strong>\u53d1\u73b0\u95ee\u9898<\/strong>\uff1a\u8bc6\u522b\u5e38\u89c1\u95ee\u9898\u3001\u670d\u52a1\u74f6\u9888\u548c\u6f5c\u5728\u98ce\u9669\u3002<\/li>\n\n\n\n<li><strong>\u6539\u8fdb\u670d\u52a1<\/strong>\uff1a\u4f18\u5316\u670d\u52a1\u6d41\u7a0b\u3001\u63d0\u9ad8\u670d\u52a1\u6548\u7387\u548c\u8d28\u91cf\u3002<\/li>\n\n\n\n<li><strong>\u63d0\u5347\u6ee1\u610f\u5ea6<\/strong>\uff1a\u89e3\u51b3\u5ba2\u6237\u95ee\u9898\u3001\u6ee1\u8db3\u5ba2\u6237\u9700\u6c42\uff0c\u63d0\u9ad8\u5ba2\u6237\u6ee1\u610f\u5ea6\u548c\u5fe0\u8bda\u5ea6\u3002<\/li>\n\n\n\n<li><strong>\u964d\u4f4e\u6210\u672c<\/strong>\uff1a\u51cf\u5c11\u91cd\u590d\u54a8\u8be2\u3001\u907f\u514d\u95ee\u9898\u5347\u7ea7\uff0c\u964d\u4f4e\u670d\u52a1\u6210\u672c\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u6570\u636e\u6765\u6e90<\/strong>\n<ul>\n<li><strong>\u5ba2\u670d\u7cfb\u7edf<\/strong>\uff1a\u7535\u8bdd\u5f55\u97f3\u3001\u804a\u5929\u8bb0\u5f55\u3001\u5de5\u5355\u7cfb\u7edf\u7b49\u3002<\/li>\n\n\n\n<li><strong>\u5728\u7ebf\u8bba\u575b<\/strong>\uff1a\u793e\u533a\u5e16\u5b50\u3001\u8bc4\u8bba\u7b49\u3002<\/li>\n\n\n\n<li><strong>\u793e\u4ea4\u5a92\u4f53<\/strong>\uff1a\u5fae\u535a\u3001\u5fae\u4fe1\u3001Facebook\u7b49\u3002<\/li>\n\n\n\n<li><strong>\u90ae\u4ef6<\/strong>\uff1a\u5ba2\u6237\u53d1\u9001\u7684\u54a8\u8be2\u548c\u6295\u8bc9\u90ae\u4ef6\u3002<\/li>\n\n\n\n<li><strong>\u7528\u6237\u8c03\u7814<\/strong>\uff1a\u95ee\u5377\u8c03\u67e5\u3001\u7528\u6237\u8bbf\u8c08\u7b49\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u5206\u6790\u65b9\u6cd5<\/strong>\n<ul>\n<li><strong>\u5173\u952e\u8bcd\u5206\u6790<\/strong>\uff1a\u7edf\u8ba1\u5173\u952e\u8bcd\u51fa\u73b0\u9891\u7387\uff0c\u4e86\u89e3\u5ba2\u6237\u5173\u6ce8\u70b9\u3002<\/li>\n\n\n\n<li><strong>\u95ee\u9898\u5206\u7c7b<\/strong>\uff1a\u5c06\u5ba2\u6237\u95ee\u9898\u5f52\u7c7b\uff0c\u8bc6\u522b\u5e38\u89c1\u95ee\u9898\u7c7b\u578b\u3002<\/li>\n\n\n\n<li><strong>\u6d41\u7a0b\u5206\u6790<\/strong>\uff1a\u5206\u6790\u670d\u52a1\u6d41\u7a0b\uff0c\u627e\u51fa\u74f6\u9888\u73af\u8282\u3002<\/li>\n\n\n\n<li><strong>\u6839\u56e0\u5206\u6790<\/strong>\uff1a\u6df1\u5165\u6316\u6398\u95ee\u9898\u6839\u6e90\uff0c\u5236\u5b9a\u89e3\u51b3\u65b9\u6848\u3002<\/li>\n\n\n\n<li><strong>\u6587\u672c\u6316\u6398<\/strong>\uff1a\u4f7f\u7528NLP\u6280\u672f\uff0c\u81ea\u52a8\u5206\u6790\u6587\u672c\u6570\u636e\uff0c\u63d0\u53d6\u5173\u952e\u4fe1\u606f\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E4%BA%8C%E3%80%81%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86NLP%E5%BA%94%E7%94%A8\"><\/span><strong>\u4e8c\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406(NLP)\u5e94\u7528<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ol>\n<li><strong>NLP\u6280\u672f\u5728\u5ba2\u6237\u670d\u52a1\u4e2d\u7684\u5e94\u7528<\/strong>\n<ul>\n<li><strong>\u81ea\u52a8\u5206\u7c7b\u5ba2\u6237\u54a8\u8be2<\/strong>\uff1a\u6839\u636e\u5ba2\u6237\u54a8\u8be2\u5185\u5bb9\uff0c\u81ea\u52a8\u5c06\u5176\u5206\u914d\u5230\u5408\u9002\u7684\u5ba2\u670d\u4eba\u5458\u6216\u90e8\u95e8\uff0c\u63d0\u9ad8\u54cd\u5e94\u901f\u5ea6\u3002<\/li>\n\n\n\n<li><strong>\u60c5\u611f\u5206\u6790<\/strong>\uff1a\u5206\u6790\u5ba2\u6237\u6587\u672c\u4fe1\u606f\u4e2d\u7684\u60c5\u611f\u503e\u5411\uff0c\u4e86\u89e3\u5ba2\u6237\u60c5\u7eea\uff0c\u6307\u5bfc\u5ba2\u670d\u4eba\u5458\u66f4\u597d\u5730\u89e3\u51b3\u95ee\u9898\u3002<\/li>\n\n\n\n<li><strong>\u667a\u80fd\u95ee\u7b54<\/strong>\uff1a\u6784\u5efa\u667a\u80fd\u95ee\u7b54\u7cfb\u7edf\uff0c\u81ea\u52a8\u89e3\u7b54\u5ba2\u6237\u5e38\u89c1\u95ee\u9898\uff0c\u51cf\u8f7b\u5ba2\u670d\u4eba\u5458\u538b\u529b\u3002<\/li>\n\n\n\n<li><strong>\u6587\u672c\u6458\u8981<\/strong>\uff1a\u81ea\u52a8\u63d0\u53d6\u5ba2\u6237\u54a8\u8be2\u7684\u5173\u952e\u4fe1\u606f\uff0c\u5e2e\u52a9\u5ba2\u670d\u4eba\u5458\u5feb\u901f\u4e86\u89e3\u95ee\u9898\u3002<\/li>\n\n\n\n<li><strong>\u673a\u5668\u7ffb\u8bd1<\/strong>\uff1a\u652f\u6301\u591a\u8bed\u8a00\u5ba2\u6237\u670d\u52a1\uff0c\u63d0\u9ad8\u670d\u52a1\u8986\u76d6\u8303\u56f4\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>NLP\u5173\u952e\u6280\u672f<\/strong>\n<ul>\n<li><strong>\u5206\u8bcd<\/strong>\uff1a\u5c06\u6587\u672c\u5206\u5272\u6210\u8bcd\u8bed\u5e8f\u5217\u3002<\/li>\n\n\n\n<li><strong>\u8bcd\u6027\u6807\u6ce8<\/strong>\uff1a\u6807\u6ce8\u8bcd\u8bed\u7684\u8bcd\u6027\uff08\u5982\u540d\u8bcd\u3001\u52a8\u8bcd\uff09\u3002<\/li>\n\n\n\n<li><strong>\u547d\u540d\u5b9e\u4f53\u8bc6\u522b<\/strong>\uff1a\u8bc6\u522b\u6587\u672c\u4e2d\u7684\u547d\u540d\u5b9e\u4f53\uff08\u5982\u4eba\u540d\u3001\u5730\u540d\u3001\u7ec4\u7ec7\u673a\u6784\u540d\uff09\u3002<\/li>\n\n\n\n<li><strong>\u60c5\u611f\u5206\u6790<\/strong>\uff1a\u5224\u65ad\u6587\u672c\u7684\u60c5\u611f\u503e\u5411\uff08\u5982\u79ef\u6781\u3001\u6d88\u6781\u3001\u4e2d\u6027\uff09\u3002<\/li>\n\n\n\n<li><strong>\u6587\u672c\u5206\u7c7b<\/strong>\uff1a\u5c06\u6587\u672c\u5f52\u7c7b\u5230\u4e0d\u540c\u7684\u7c7b\u522b\u3002<\/li>\n\n\n\n<li><strong>\u6587\u672c\u6458\u8981<\/strong>\uff1a\u63d0\u53d6\u6587\u672c\u7684\u5173\u952e\u4fe1\u606f\uff0c\u751f\u6210\u6458\u8981\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E4%B8%89%E3%80%81%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90%E5%B8%AE%E5%8A%A9%E7%90%86%E8%A7%A3%E9%A1%BE%E5%AE%A2%E6%83%85%E7%BB%AA\"><\/span><strong>\u4e09\u3001\u60c5\u611f\u5206\u6790\u5e2e\u52a9\u7406\u89e3\u987e\u5ba2\u60c5\u7eea<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ol>\n<li><strong>\u60c5\u611f\u5206\u6790\u7684\u610f\u4e49<\/strong>\n<ul>\n<li><strong>\u4e86\u89e3\u5ba2\u6237\u60c5\u7eea<\/strong>\uff1a\u5e2e\u52a9\u4f01\u4e1a\u4e86\u89e3\u5ba2\u6237\u5bf9\u4ea7\u54c1\u3001\u670d\u52a1\u548c\u54c1\u724c\u7684\u6001\u5ea6\u3002<\/li>\n\n\n\n<li><strong>\u6307\u5bfc\u5ba2\u670d\u884c\u4e3a<\/strong>\uff1a\u6307\u5bfc\u5ba2\u670d\u4eba\u5458\u6839\u636e\u5ba2\u6237\u60c5\u7eea\u8c03\u6574\u6c9f\u901a\u65b9\u5f0f\uff0c\u63d0\u9ad8\u95ee\u9898\u89e3\u51b3\u6548\u7387\u3002<\/li>\n\n\n\n<li><strong>\u9884\u8b66\u6f5c\u5728\u98ce\u9669<\/strong>\uff1a\u53ca\u65f6\u53d1\u73b0\u5ba2\u6237\u4e0d\u6ee1\u60c5\u7eea\uff0c\u907f\u514d\u95ee\u9898\u5347\u7ea7\u3002<\/li>\n\n\n\n<li><strong>\u4f18\u5316\u4ea7\u54c1\u548c\u670d\u52a1<\/strong>\uff1a\u6839\u636e\u5ba2\u6237\u60c5\u611f\u53cd\u9988\uff0c\u6539\u8fdb\u4ea7\u54c1\u548c\u670d\u52a1\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u60c5\u611f\u5206\u6790\u65b9\u6cd5<\/strong>\n<ul>\n<li><strong>\u57fa\u4e8e\u8bcd\u5178\u7684\u65b9\u6cd5<\/strong>\uff1a\u6784\u5efa\u60c5\u611f\u8bcd\u5178\uff0c\u6839\u636e\u6587\u672c\u4e2d\u60c5\u611f\u8bcd\u8bed\u7684\u6570\u91cf\u548c\u6743\u91cd\uff0c\u5224\u65ad\u6587\u672c\u7684\u60c5\u611f\u503e\u5411\u3002<\/li>\n\n\n\n<li><strong>\u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u65b9\u6cd5<\/strong>\uff1a\u4f7f\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u8bad\u7ec3\u60c5\u611f\u5206\u7c7b\u6a21\u578b\uff0c\u5bf9\u6587\u672c\u8fdb\u884c\u60c5\u611f\u5206\u7c7b\u3002<\/li>\n\n\n\n<li><strong>\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5<\/strong>\uff1a\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u5982\u5faa\u73af\u795e\u7ecf\u7f51\u7edc(RNN)\u548cTransformer\uff0c\u8fdb\u884c\u60c5\u611f\u5206\u6790\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u60c5\u611f\u5206\u6790\u5de5\u5177<\/strong>\n<ul>\n<li><strong>VADER (Valence Aware Dictionary and sEntiment Reasoner)<\/strong>&nbsp;\uff1a\u57fa\u4e8e\u8bcd\u5178\u7684\u60c5\u611f\u5206\u6790\u5de5\u5177\uff0c\u7b80\u5355\u6613\u7528\u3002<\/li>\n\n\n\n<li><strong>TextBlob<\/strong>\uff1a\u57fa\u4e8eNLTK\u7684\u6587\u672c\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u60c5\u611f\u5206\u6790\u529f\u80fd\u3002<\/li>\n\n\n\n<li><strong>\u767e\u5ea6AI\u5f00\u653e\u5e73\u53f0<\/strong>\uff1a\u63d0\u4f9b\u60c5\u611f\u503e\u5411\u5206\u6790API\u3002<\/li>\n\n\n\n<li><strong>\u963f\u91cc\u4e91\u81ea\u7136\u8bed\u8a00\u5904\u7406<\/strong>\uff1a\u63d0\u4f9b\u60c5\u611f\u5206\u6790\u670d\u52a1\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"%E5%9B%9B%E3%80%81%E5%AE%9E%E6%88%98%E6%A1%88%E4%BE%8B%EF%BC%9A%E5%88%9B%E5%BB%BA%E4%B8%80%E4%B8%AA%E8%81%8A%E5%A4%A9%E6%9C%BA%E5%99%A8%E4%BA%BA%E5%8E%9F%E5%9E%8B\"><\/span><strong>\u56db\u3001\u5b9e\u6218\u6848\u4f8b\uff1a\u521b\u5efa\u4e00\u4e2a\u804a\u5929\u673a\u5668\u4eba\u539f\u578b<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ul>\n<li><strong>\u76ee\u6807<\/strong>\uff1a\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u804a\u5929\u673a\u5668\u4eba\u539f\u578b\uff0c\u7528\u4e8e\u89e3\u7b54\u5e38\u89c1\u5ba2\u6237\u7591\u95ee\uff0c\u6539\u5584\u7528\u6237\u4f53\u9a8c\u3002<\/li>\n\n\n\n<li><strong>\u6d41\u7a0b<\/strong>\uff1a\n<ol>\n<li><strong>\u6536\u96c6\u5e38\u89c1\u95ee\u9898<\/strong>\uff1a\u6574\u7406\u5ba2\u6237\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u7b54\u6848\u3002<\/li>\n\n\n\n<li><strong>\u6784\u5efa\u77e5\u8bc6\u5e93<\/strong>\uff1a\u5c06\u95ee\u9898\u548c\u7b54\u6848\u5b58\u50a8\u5728\u77e5\u8bc6\u5e93\u4e2d\u3002<\/li>\n\n\n\n<li><strong>\u6587\u672c\u9884\u5904\u7406<\/strong>\uff1a\u5bf9\u95ee\u9898\u8fdb\u884c\u5206\u8bcd\u3001\u53bb\u9664\u505c\u7528\u8bcd\u7b49\u5904\u7406\u3002<\/li>\n\n\n\n<li><strong>\u76f8\u4f3c\u5ea6\u8ba1\u7b97<\/strong>\uff1a\u8ba1\u7b97\u7528\u6237\u8f93\u5165\u95ee\u9898\u4e0e\u77e5\u8bc6\u5e93\u4e2d\u95ee\u9898\u7684\u76f8\u4f3c\u5ea6\u3002<\/li>\n\n\n\n<li><strong>\u7b54\u6848\u5339\u914d<\/strong>\uff1a\u627e\u5230\u4e0e\u7528\u6237\u8f93\u5165\u95ee\u9898\u6700\u76f8\u4f3c\u7684\u95ee\u9898\uff0c\u5e76\u8fd4\u56de\u5bf9\u5e94\u7684\u7b54\u6848\u3002<\/li>\n\n\n\n<li><strong>\u7528\u6237\u4ea4\u4e92<\/strong>\uff1a\u4e0e\u7528\u6237\u8fdb\u884c\u4ea4\u4e92\uff0c\u63a5\u6536\u7528\u6237\u8f93\u5165\uff0c\u8fd4\u56de\u7b54\u6848\u3002<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Python%E5%AE%9E%E8%B7%B5%E6%A1%88%E4%BE%8B\"><\/span>Python\u5b9e\u8df5\u6848\u4f8b <span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>\u8bf7\u5c06\u4ee5\u4e0b\u6240\u6709\u4ee3\u7801\u590d\u5236\u5230Jupyter Notebook\u7684\u4e00\u4e2a\u5355\u5143\u683c\u4e2d\uff0c\u7136\u540e\u8fd0\u884c\u5373\u53ef\u3002<\/p>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># ===================================================================\n# \u5ba2\u6237\u670d\u52a1\u8d28\u91cf\u63d0\u5347\u4e0e\u6ee1\u610f\u5ea6\u8c03\u67e5 - Python\u5b9e\u8df5\u6848\u4f8b\n# ===================================================================\n\n# ---------------------------------\n# \u6b65\u9aa4 1: \u5bfc\u5165\u6240\u9700\u5e93\n# ---------------------------------\nimport pandas as pd\nimport numpy as np\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport warnings\n\nwarnings.filterwarnings('ignore')\nnltk.download('punkt')\nnltk.download('stopwords')\n\nprint(\"--- \u6b65\u9aa4 1: \u5e93\u5bfc\u5165\u6210\u529f ---\")\n\n# ---------------------------------\n# \u6b65\u9aa4 2: \u521b\u5efa\u5e38\u89c1\u95ee\u9898\u77e5\u8bc6\u5e93\n# ---------------------------------\nknowledge_base = {\n    \"\u5982\u4f55\u9000\u8d27\uff1f\": \"\u60a8\u53ef\u4ee5\u5728\u6536\u5230\u5546\u54c1\u540e\u76847\u5929\u5185\u7533\u8bf7\u9000\u8d27\uff0c\u8bf7\u786e\u4fdd\u5546\u54c1\u672a\u4f7f\u7528\u4e14\u5305\u88c5\u5b8c\u597d\u3002\",\n    \"\u8fd0\u8d39\u5982\u4f55\u8ba1\u7b97\uff1f\": \"\u8fd0\u8d39\u6839\u636e\u60a8\u7684\u8ba2\u5355\u91d1\u989d\u548c\u6536\u8d27\u5730\u5740\u8ba1\u7b97\uff0c\u5177\u4f53\u8d39\u7528\u8bf7\u5728\u4e0b\u5355\u65f6\u67e5\u770b\u3002\",\n    \"\u5982\u4f55\u4fee\u6539\u8ba2\u5355\uff1f\": \"\u5982\u679c\u60a8\u9700\u8981\u4fee\u6539\u8ba2\u5355\uff0c\u8bf7\u5728\u4e0b\u5355\u540e2\u5c0f\u65f6\u5185\u8054\u7cfb\u5ba2\u670d\uff0c\u6211\u4eec\u5c06\u5c3d\u5feb\u4e3a\u60a8\u5904\u7406\u3002\",\n    \"\u5546\u54c1\u591a\u4e45\u80fd\u9001\u5230\uff1f\": \"\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u5546\u54c1\u4f1a\u57283-5\u4e2a\u5de5\u4f5c\u65e5\u5185\u9001\u8fbe\uff0c\u504f\u8fdc\u5730\u533a\u53ef\u80fd\u9700\u8981\u66f4\u957f\u65f6\u95f4\u3002\",\n    \"\u5982\u4f55\u8054\u7cfb\u5ba2\u670d\uff1f\": \"\u60a8\u53ef\u4ee5\u62e8\u6253\u6211\u4eec\u7684\u5ba2\u670d\u7535\u8bdd400-xxx-xxxx\uff0c\u6216\u5728\u7f51\u7ad9\u4e0a\u70b9\u51fb\u201c\u5728\u7ebf\u5ba2\u670d\u201d\u6309\u94ae\u3002\"\n}\n\nprint(\"\\n--- \u6b65\u9aa4 2: \u5e38\u89c1\u95ee\u9898\u77e5\u8bc6\u5e93\u521b\u5efa\u6210\u529f ---\")\nprint(\"\u77e5\u8bc6\u5e93\u5185\u5bb9:\")\nprint(knowledge_base)\n\n# ---------------------------------\n# \u6b65\u9aa4 3: \u6587\u672c\u9884\u5904\u7406\n# ---------------------------------\nprint(\"\\n--- \u6b65\u9aa4 3: \u6587\u672c\u9884\u5904\u7406 ---\")\n\nstop_words = set(stopwords.words('chinese'))  # \u4f7f\u7528\u4e2d\u6587\u505c\u7528\u8bcd\n\ndef preprocess_text(text):\n    word_tokens = word_tokenize(text)\n    filtered_sentence = &#91;w for w in word_tokens if not w in stop_words and w.isalnum()]\n    return \" \".join(filtered_sentence)\n\n# \u9884\u5904\u7406\u77e5\u8bc6\u5e93\u4e2d\u7684\u95ee\u9898\npreprocessed_questions = &#91;preprocess_text(q) for q in knowledge_base.keys()]\n\nprint(\"\\n\u9884\u5904\u7406\u540e\u7684\u95ee\u9898:\")\nprint(preprocessed_questions)\n\n# ---------------------------------\n# \u6b65\u9aa4 4: \u8ba1\u7b97\u6587\u672c\u76f8\u4f3c\u5ea6\n# ---------------------------------\nprint(\"\\n--- \u6b65\u9aa4 4: \u8ba1\u7b97\u6587\u672c\u76f8\u4f3c\u5ea6 ---\")\n\n# \u4f7f\u7528TF-IDF\u5411\u91cf\u5316\nvectorizer = TfidfVectorizer()\ntfidf_matrix = vectorizer.fit_transform(preprocessed_questions)\n\ndef get_similarity(user_input):\n    preprocessed_input = preprocess_text(user_input)\n    input_vector = vectorizer.transform(&#91;preprocessed_input])\n    similarity_scores = cosine_similarity(input_vector, tfidf_matrix)\n    return similarity_scores\n\n# ---------------------------------\n# \u6b65\u9aa4 5: \u7b54\u6848\u5339\u914d\n# ---------------------------------\nprint(\"\\n--- \u6b65\u9aa4 5: \u7b54\u6848\u5339\u914d ---\")\n\ndef get_answer(user_input):\n    similarity_scores = get_similarity(user_input)\n    best_match_index = np.argmax(similarity_scores)\n    best_match_question = list(knowledge_base.keys())&#91;best_match_index]\n    return knowledge_base&#91;best_match_question]\n\n# ---------------------------------\n# \u6b65\u9aa4 6: \u521b\u5efa\u804a\u5929\u673a\u5668\u4eba\u539f\u578b\n# ---------------------------------\nprint(\"\\n--- \u6b65\u9aa4 6: \u521b\u5efa\u804a\u5929\u673a\u5668\u4eba\u539f\u578b ---\")\n\ndef chatbot():\n    print(\"\u60a8\u597d\uff0c\u6211\u662f\u5ba2\u670d\u673a\u5668\u4eba\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u60a8\uff1f\")\n    while True:\n        user_input = input(\"\u60a8\uff1a\")\n        if user_input.lower() == \"\u9000\u51fa\":\n            print(\"\u518d\u89c1\uff01\")\n            break\n        answer = get_answer(user_input)\n        print(\"\u673a\u5668\u4eba\uff1a\", answer)\n\n# \u8fd0\u884c\u804a\u5929\u673a\u5668\u4eba\nchatbot()\n\nprint(\"\\n--- \u6240\u6709\u6b65\u9aa4\u6267\u884c\u5b8c\u6bd5 ---\")<\/code><\/pre>\n\n\n\n<p><strong>\u4ee3\u7801\u89e3\u91ca:<\/strong><\/p>\n\n\n\n<ol>\n<li><strong>\u5bfc\u5165\u6240\u9700\u5e93<\/strong>: \u5bfc\u5165\u6570\u636e\u5904\u7406\u3001\u6587\u672c\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u6240\u9700\u7684Python\u5e93\u3002<\/li>\n\n\n\n<li><strong>\u521b\u5efa\u5e38\u89c1\u95ee\u9898\u77e5\u8bc6\u5e93<\/strong>: \u521b\u5efa\u4e00\u4e2a\u5b57\u5178\uff0c\u5b58\u50a8\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u7b54\u6848\u3002<\/li>\n\n\n\n<li><strong>\u6587\u672c\u9884\u5904\u7406<\/strong>:\n<ul>\n<li>\u5b9a\u4e49\u505c\u7528\u8bcd\u5217\u8868\uff0c\u53bb\u9664\u6587\u672c\u4e2d\u7684\u505c\u7528\u8bcd\u3002<\/li>\n\n\n\n<li>\u5bf9\u7528\u6237\u8f93\u5165\u7684\u95ee\u9898\u8fdb\u884c\u5206\u8bcd\u3001\u53bb\u9664\u505c\u7528\u8bcd\u7b49\u5904\u7406\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u8ba1\u7b97\u6587\u672c\u76f8\u4f3c\u5ea6<\/strong>:\n<ul>\n<li>\u4f7f\u7528TF-IDF\u5411\u91cf\u5316\u6280\u672f\uff0c\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u5411\u91cf\u8868\u793a\u3002<\/li>\n\n\n\n<li>\u8ba1\u7b97\u7528\u6237\u8f93\u5165\u95ee\u9898\u4e0e\u77e5\u8bc6\u5e93\u4e2d\u95ee\u9898\u7684\u76f8\u4f3c\u5ea6\uff08\u4f7f\u7528\u4f59\u5f26\u76f8\u4f3c\u5ea6\uff09\u3002<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>\u7b54\u6848\u5339\u914d<\/strong>: \u627e\u5230\u4e0e\u7528\u6237\u8f93\u5165\u95ee\u9898\u6700\u76f8\u4f3c\u7684\u95ee\u9898\uff0c\u5e76\u8fd4\u56de\u5bf9\u5e94\u7684\u7b54\u6848\u3002<\/li>\n\n\n\n<li><strong>\u521b\u5efa\u804a\u5929\u673a\u5668\u4eba\u539f\u578b<\/strong>:\n<ul>\n<li>\u7f16\u5199\u4e00\u4e2a\u5faa\u73af\uff0c\u63a5\u6536\u7528\u6237\u8f93\u5165\uff0c\u8c03\u7528<code>get_answer()<\/code>\u51fd\u6570\u83b7\u53d6\u7b54\u6848\uff0c\u5e76\u8f93\u51fa\u5230\u63a7\u5236\u53f0\u3002<\/li>\n\n\n\n<li>\u5f53\u7528\u6237\u8f93\u5165\u201c\u9000\u51fa\u201d\u65f6\uff0c\u7ed3\u675f\u5faa\u73af\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<p><strong>\u8fd0\u884c\u8bf4\u660e:<\/strong><\/p>\n\n\n\n<ul>\n<li>\u5c06\u4ee3\u7801\u590d\u5236\u5230Jupyter Notebook\u7684\u4e00\u4e2a\u5355\u5143\u683c\u4e2d\u3002<\/li>\n\n\n\n<li>\u9010\u4e2a\u8fd0\u884c\u6bcf\u4e2a\u4ee3\u7801\u5757\uff0c\u89c2\u5bdf\u8f93\u51fa\u7ed3\u679c\u3002<\/li>\n\n\n\n<li>\u8fd0\u884c<code>chatbot()<\/code>\u51fd\u6570\uff0c\u4e0e\u804a\u5929\u673a\u5668\u4eba\u8fdb\u884c\u4ea4\u4e92\u3002<\/li>\n\n\n\n<li>\u53ef\u4ee5\u4fee\u6539\u77e5\u8bc6\u5e93\u4e2d\u7684\u95ee\u9898\u548c\u7b54\u6848\uff0c\u4ee5\u6269\u5c55\u804a\u5929\u673a\u5668\u4eba\u7684\u529f\u80fd\u3002<\/li>\n\n\n\n<li>\u53ef\u4ee5\u5c1d\u8bd5\u4e0d\u540c\u7684\u6587\u672c\u9884\u5904\u7406\u65b9\u6cd5\u548c\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u65b9\u6cd5\uff0c\u4ee5\u63d0\u9ad8\u804a\u5929\u673a\u5668\u4eba\u7684\u51c6\u786e\u6027\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u8fd9\u4e2a\u5b9e\u8df5\u6848\u4f8b\u63d0\u4f9b\u4e86\u4e00\u4e2a\u57fa\u672c\u7684\u804a\u5929\u673a\u5668\u4eba\u539f\u578b\u7684\u6846\u67b6\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u4e1a\u52a1\u9700\u6c42\u8fdb\u884c\u66f4\u6df1\u5165\u7684\u5f00\u53d1\uff0c\u4f8b\u5982\uff1a<\/p>\n\n\n\n<ul>\n<li><strong>\u4f7f\u7528\u66f4\u9ad8\u7ea7\u7684NLP\u6280\u672f<\/strong>\uff1a\u5982word2vec\u3001BERT\u7b49\uff0c\u63d0\u9ad8\u6587\u672c\u76f8\u4f3c\u5ea6\u8ba1\u7b97\u7684\u51c6\u786e\u6027\u3002<\/li>\n\n\n\n<li><strong>\u589e\u52a0\u5bf9\u8bdd\u7ba1\u7406\u529f\u80fd<\/strong>\uff1a\u8bb0\u5f55\u7528\u6237\u5bf9\u8bdd\u5386\u53f2\uff0c\u5b9e\u73b0\u66f4\u81ea\u7136\u7684\u5bf9\u8bdd\u6d41\u7a0b\u3002<\/li>\n\n\n\n<li><strong>\u96c6\u6210\u5230Web\u6216App\u4e2d<\/strong>\uff1a\u5c06\u804a\u5929\u673a\u5668\u4eba\u90e8\u7f72\u5230Web\u6216App\u4e2d\uff0c\u63d0\u4f9b\u66f4\u4fbf\u6377\u7684\u5ba2\u6237\u670d\u52a1\u3002<\/li>\n\n\n\n<li><strong>\u4e0e\u4eba\u5de5\u5ba2\u670d\u5bf9\u63a5<\/strong>\uff1a\u5f53\u804a\u5929\u673a\u5668\u4eba\u65e0\u6cd5\u56de\u7b54\u7528\u6237\u95ee\u9898\u65f6\uff0c\u8f6c\u63a5\u5230\u4eba\u5de5\u5ba2\u670d\u3002<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>\u5982\u679c\u6ca1\u6709&nbsp;Punkt tokenizer \u6a21\u578b\uff0c\u53ef\u4ee5\u76f4\u63a5\u4e0b\u8f7d<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>try:\n    nltk.data.find('tokenizers\/punkt')\nexcept LookupError:\n    print(\"Downloading punkt tokenizer...\")\n    nltk.download('punkt')\n\ntry:\n    nltk.data.find('corpora\/stopwords')\nexcept LookupError:\n    print(\"Downloading stopwords...\")\n    nltk.download('stopwords')\n\n\n\u6216\u6362\u4f7f\u7528 jieba \u5206\u8bcd\u5668\n\n# ===================================================================\n# \u6a21\u5757\u4e94\uff1a\u5ba2\u6237\u670d\u52a1\u8d28\u91cf\u63d0\u5347\u4e0e\u6ee1\u610f\u5ea6\u8c03\u67e5 - Python\u5b9e\u8df5\u6848\u4f8b\n# ===================================================================\n\n# ---------------------------------\n# \u6b65\u9aa4 1: \u5bfc\u5165\u6240\u9700\u5e93\n# ---------------------------------\nimport pandas as pd\nimport numpy as np\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport warnings\nimport os\n\nwarnings.filterwarnings('ignore')\n\n# \u8bbe\u7f6e NLTK \u6570\u636e\u8def\u5f84\nnltk_data_path = os.path.join(os.getcwd(), 'nltk_data')  # \u5c06\u6570\u636e\u4e0b\u8f7d\u5230\u5f53\u524d\u5de5\u4f5c\u76ee\u5f55\nif not os.path.exists(nltk_data_path):\n    os.makedirs(nltk_data_path)\nnltk.data.path.append(nltk_data_path)\n\n# \u4e0b\u8f7d NLTK \u6570\u636e (\u5982\u679c\u5c1a\u672a\u4e0b\u8f7d)\ntry:\n    nltk.data.find('tokenizers\/punkt')\nexcept LookupError:\n    print(\"Downloading punkt tokenizer...\")\n    nltk.download('punkt', download_dir=nltk_data_path)\n\ntry:\n    nltk.data.find('corpora\/stopwords')\nexcept LookupError:\n    print(\"Downloading stopwords...\")\n    nltk.download('stopwords', download_dir=nltk_data_path)\n\n\nprint(\"--- \u6b65\u9aa4 1: \u5e93\u5bfc\u5165\u6210\u529f ---\")\n\n# ---------------------------------\n# \u6b65\u9aa4 2: \u521b\u5efa\u5e38\u89c1\u95ee\u9898\u77e5\u8bc6\u5e93\n# ---------------------------------\nknowledge_base = {\n    \"\u5982\u4f55\u9000\u8d27\uff1f\": \"\u60a8\u53ef\u4ee5\u5728\u6536\u5230\u5546\u54c1\u540e\u76847\u5929\u5185\u7533\u8bf7\u9000\u8d27\uff0c\u8bf7\u786e\u4fdd\u5546\u54c1\u672a\u4f7f\u7528\u4e14\u5305\u88c5\u5b8c\u597d\u3002\",\n    \"\u8fd0\u8d39\u5982\u4f55\u8ba1\u7b97\uff1f\": \"\u8fd0\u8d39\u6839\u636e\u60a8\u7684\u8ba2\u5355\u91d1\u989d\u548c\u6536\u8d27\u5730\u5740\u8ba1\u7b97\uff0c\u5177\u4f53\u8d39\u7528\u8bf7\u5728\u4e0b\u5355\u65f6\u67e5\u770b\u3002\",\n    \"\u5982\u4f55\u4fee\u6539\u8ba2\u5355\uff1f\": \"\u5982\u679c\u60a8\u9700\u8981\u4fee\u6539\u8ba2\u5355\uff0c\u8bf7\u5728\u4e0b\u5355\u540e2\u5c0f\u65f6\u5185\u8054\u7cfb\u5ba2\u670d\uff0c\u6211\u4eec\u5c06\u5c3d\u5feb\u4e3a\u60a8\u5904\u7406\u3002\",\n    \"\u5546\u54c1\u591a\u4e45\u80fd\u9001\u5230\uff1f\": \"\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u5546\u54c1\u4f1a\u57283-5\u4e2a\u5de5\u4f5c\u65e5\u5185\u9001\u8fbe\uff0c\u504f\u8fdc\u5730\u533a\u53ef\u80fd\u9700\u8981\u66f4\u957f\u65f6\u95f4\u3002\",\n    \"\u5982\u4f55\u8054\u7cfb\u5ba2\u670d\uff1f\": \"\u60a8\u53ef\u4ee5\u62e8\u6253\u6211\u4eec\u7684\u5ba2\u670d\u7535\u8bdd400-xxx-xxxx\uff0c\u6216\u5728\u7f51\u7ad9\u4e0a\u70b9\u51fb\u201c\u5728\u7ebf\u5ba2\u670d\u201d\u6309\u94ae\u3002\"\n}\n\nprint(\"\\n--- \u6b65\u9aa4 2: \u5e38\u89c1\u95ee\u9898\u77e5\u8bc6\u5e93\u521b\u5efa\u6210\u529f ---\")\nprint(\"\u77e5\u8bc6\u5e93\u5185\u5bb9:\")\nprint(knowledge_base)\n\n# ---------------------------------\n# \u6b65\u9aa4 3: \u6587\u672c\u9884\u5904\u7406\n# ---------------------------------\nprint(\"\\n--- \u6b65\u9aa4 3: \u6587\u672c\u9884\u5904\u7406 ---\")\n\n#  \u4f7f\u7528 jieba \u5206\u8bcd (\u66f4\u9002\u5408\u4e2d\u6587)\nimport jieba\ndef preprocess_text(text):\n    word_tokens = jieba.cut(text)  # \u4f7f\u7528 jieba \u5206\u8bcd\n    stop_words = set(stopwords.words('chinese')) # \u4f7f\u7528\u4e2d\u6587\u505c\u7528\u8bcd\n    filtered_sentence = &#91;w for w in word_tokens if not w in stop_words and w.isalnum()]\n    return \" \".join(filtered_sentence)\n\n# \u9884\u5904\u7406\u77e5\u8bc6\u5e93\u4e2d\u7684\u95ee\u9898\npreprocessed_questions = &#91;preprocess_text(q) for q in knowledge_base.keys()]\n\nprint(\"\\n\u9884\u5904\u7406\u540e\u7684\u95ee\u9898:\")\nprint(preprocessed_questions)\n\n# ---------------------------------\n# \u6b65\u9aa4 4: \u8ba1\u7b97\u6587\u672c\u76f8\u4f3c\u5ea6\n# ---------------------------------\nprint(\"\\n--- \u6b65\u9aa4 4: \u8ba1\u7b97\u6587\u672c\u76f8\u4f3c\u5ea6 ---\")\n\n# \u4f7f\u7528TF-IDF\u5411\u91cf\u5316\nvectorizer = TfidfVectorizer()\ntfidf_matrix = vectorizer.fit_transform(preprocessed_questions)\n\ndef get_similarity(user_input):\n    preprocessed_input = preprocess_text(user_input)\n    input_vector = vectorizer.transform(&#91;preprocessed_input])\n    similarity_scores = cosine_similarity(input_vector, tfidf_matrix)\n    return similarity_scores\n\n# ---------------------------------\n# \u6b65\u9aa4 5: \u7b54\u6848\u5339\u914d\n# ---------------------------------\nprint(\"\\n--- \u6b65\u9aa4 5: \u7b54\u6848\u5339\u914d ---\")\n\ndef get_answer(user_input):\n    similarity_scores = get_similarity(user_input)\n    best_match_index = np.argmax(similarity_scores)\n    best_match_question = list(knowledge_base.keys())&#91;best_match_index]\n    return knowledge_base&#91;best_match_question]\n\n# ---------------------------------\n# \u6b65\u9aa4 6: \u521b\u5efa\u804a\u5929\u673a\u5668\u4eba\u539f\u578b\n# ---------------------------------\nprint(\"\\n--- \u6b65\u9aa4 6: \u521b\u5efa\u804a\u5929\u673a\u5668\u4eba\u539f\u578b ---\")\n\ndef chatbot():\n    print(\"\u60a8\u597d\uff0c\u6211\u662f\u5ba2\u670d\u673a\u5668\u4eba\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u60a8\uff1f\")\n    while True:\n        user_input = input(\"\u60a8\uff1a\")\n        if user_input.lower() == \"\u9000\u51fa\":\n            print(\"\u518d\u89c1\uff01\")\n            break\n        answer = get_answer(user_input)\n        print(\"\u673a\u5668\u4eba\uff1a\", answer)\n\n# \u8fd0\u884c\u804a\u5929\u673a\u5668\u4eba\nchatbot()\n\nprint(\"\\n--- \u6240\u6709\u6b65\u9aa4\u6267\u884c\u5b8c\u6bd5 ---\")<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>\u8fd0\u884c\u7ed3\u679c\uff1a<\/p>\n\n\n\n<p>&#8212; \u6b65\u9aa4 1: \u5e93\u5bfc\u5165\u6210\u529f &#8212;<\/p>\n\n\n\n<p>&#8212; \u6b65\u9aa4 2: \u5e38\u89c1\u95ee\u9898\u77e5\u8bc6\u5e93\u521b\u5efa\u6210\u529f &#8212;<br>\u77e5\u8bc6\u5e93\u5185\u5bb9:<br>{&#8216;\u5982\u4f55\u9000\u8d27\uff1f&#8217;: &#8216;\u60a8\u53ef\u4ee5\u5728\u6536\u5230\u5546\u54c1\u540e\u76847\u5929\u5185\u7533\u8bf7\u9000\u8d27\uff0c\u8bf7\u786e\u4fdd\u5546\u54c1\u672a\u4f7f\u7528\u4e14\u5305\u88c5\u5b8c\u597d\u3002&#8217;, &#8216;\u8fd0\u8d39\u5982\u4f55\u8ba1\u7b97\uff1f&#8217;: &#8216;\u8fd0\u8d39\u6839\u636e\u60a8\u7684\u8ba2\u5355\u91d1\u989d\u548c\u6536\u8d27\u5730\u5740\u8ba1\u7b97\uff0c\u5177\u4f53\u8d39\u7528\u8bf7\u5728\u4e0b\u5355\u65f6\u67e5\u770b\u3002&#8217;, &#8216;\u5982\u4f55\u4fee\u6539\u8ba2\u5355\uff1f&#8217;: &#8216;\u5982\u679c\u60a8\u9700\u8981\u4fee\u6539\u8ba2\u5355\uff0c\u8bf7\u5728\u4e0b\u5355\u540e2\u5c0f\u65f6\u5185\u8054\u7cfb\u5ba2\u670d\uff0c\u6211\u4eec\u5c06\u5c3d\u5feb\u4e3a\u60a8\u5904\u7406\u3002&#8217;, &#8216;\u5546\u54c1\u591a\u4e45\u80fd\u9001\u5230\uff1f&#8217;: &#8216;\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u5546\u54c1\u4f1a\u57283-5\u4e2a\u5de5\u4f5c\u65e5\u5185\u9001\u8fbe\uff0c\u504f\u8fdc\u5730\u533a\u53ef\u80fd\u9700\u8981\u66f4\u957f\u65f6\u95f4\u3002&#8217;, &#8216;\u5982\u4f55\u8054\u7cfb\u5ba2\u670d\uff1f&#8217;: &#8216;\u60a8\u53ef\u4ee5\u62e8\u6253\u6211\u4eec\u7684\u5ba2\u670d\u7535\u8bdd400-xxx-xxxx\uff0c\u6216\u5728\u7f51\u7ad9\u4e0a\u70b9\u51fb\u201c\u5728\u7ebf\u5ba2\u670d\u201d\u6309\u94ae\u3002&#8217;}<\/p>\n\n\n\n<p>&#8212; \u6b65\u9aa4 3: \u6587\u672c\u9884\u5904\u7406 &#8212;<br>Building prefix dict from the default dictionary \u2026<br>Loading model cost 0.963 seconds.<br>Prefix dict has been built successfully.<\/p>\n\n\n\n<p>\u9884\u5904\u7406\u540e\u7684\u95ee\u9898:<br>[&#8216;\u9000\u8d27&#8217;, &#8216;\u8fd0\u8d39 \u8ba1\u7b97&#8217;, &#8216;\u4fee\u6539 \u8ba2\u5355&#8217;, &#8216;\u5546\u54c1 \u591a\u4e45 \u9001\u5230&#8217;, &#8216;\u5ba2\u670d&#8217;]<\/p>\n\n\n\n<p>&#8212; \u6b65\u9aa4 4: \u8ba1\u7b97\u6587\u672c\u76f8\u4f3c\u5ea6 &#8212;<\/p>\n\n\n\n<p>&#8212; \u6b65\u9aa4 5: \u7b54\u6848\u5339\u914d &#8212;<\/p>\n\n\n\n<p>&#8212; \u6b65\u9aa4 6: \u521b\u5efa\u804a\u5929\u673a\u5668\u4eba\u539f\u578b &#8212;<br>\u60a8\u597d\uff0c\u6211\u662f\u5ba2\u670d\u673a\u5668\u4eba\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u60a8\uff1f<br>\u673a\u5668\u4eba\uff1a \u60a8\u53ef\u4ee5\u5728\u6536\u5230\u5546\u54c1\u540e\u76847\u5929\u5185\u7533\u8bf7\u9000\u8d27\uff0c\u8bf7\u786e\u4fdd\u5546\u54c1\u672a\u4f7f\u7528\u4e14\u5305\u88c5\u5b8c\u597d\u3002<br>\u673a\u5668\u4eba\uff1a \u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u5546\u54c1\u4f1a\u57283-5\u4e2a\u5de5\u4f5c\u65e5\u5185\u9001\u8fbe\uff0c\u504f\u8fdc\u5730\u533a\u53ef\u80fd\u9700\u8981\u66f4\u957f\u65f6\u95f4\u3002<br>\u673a\u5668\u4eba\uff1a \u60a8\u53ef\u4ee5\u5728\u6536\u5230\u5546\u54c1\u540e\u76847\u5929\u5185\u7533\u8bf7\u9000\u8d27\uff0c\u8bf7\u786e\u4fdd\u5546\u54c1\u672a\u4f7f\u7528\u4e14\u5305\u88c5\u5b8c\u597d\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5ba2\u6237\u670d\u52a1\u8d28\u91cf\u63d0\u5347\u4e0e\u6ee1\u610f\u5ea6\u8c03\u67e5 \u4e00\u3001\u5ba2\u6237\u670d\u52a1\u6539\u8fdb\u70b9\u8bc6\u522b \u4e8c\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406(NLP)\u5e94\u7528 \u4e09\u3001\u60c5\u611f\u5206\u6790\u5e2e&hellip; <a href=\"http:\/\/viplao.com\/index.php\/2025\/10\/11\/%e3%80%90python%e5%ae%9e%e8%b7%b5%e6%a1%88%e4%be%8b%e3%80%91%e5%ae%a2%e6%88%b7%e6%9c%8d%e5%8a%a1%e8%b4%a8%e9%87%8f%e6%8f%90%e5%8d%87%e4%b8%8e%e6%bb%a1%e6%84%8f%e5%ba%a6%e8%b0%83%e6%9f%a5%e5%ae%9e\/\" class=\"more-link read-more\" rel=\"bookmark\">\u7ee7\u7eed\u9605\u8bfb <span class=\"screen-reader-text\">\u3010Python\u5b9e\u8df5\u6848\u4f8b\u3011\u5ba2\u6237\u670d\u52a1\u8d28\u91cf\u63d0\u5347\u4e0e\u6ee1\u610f\u5ea6\u8c03\u67e5\u5b9e\u8df5<\/span><i class=\"fa fa-arrow-right\"><\/i><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[28],"views":548,"_links":{"self":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4040"}],"collection":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/comments?post=4040"}],"version-history":[{"count":3,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4040\/revisions"}],"predecessor-version":[{"id":4044,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/posts\/4040\/revisions\/4044"}],"wp:attachment":[{"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/media?parent=4040"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/categories?post=4040"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/viplao.com\/index.php\/wp-json\/wp\/v2\/tags?post=4040"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}