{"id":2045,"date":"2026-05-11T21:24:06","date_gmt":"2026-05-11T13:24:06","guid":{"rendered":"https:\/\/dianshudata.com\/story\/2026\/05\/11\/xiaohongshu-creator-influence-report\/"},"modified":"2026-05-11T21:28:03","modified_gmt":"2026-05-11T13:28:03","slug":"xiaohongshu-creator-influence-report","status":"publish","type":"post","link":"https:\/\/dianshudata.com\/story\/2026\/05\/11\/xiaohongshu-creator-influence-report\/","title":{"rendered":"2025\u5e74\u5c0f\u7ea2\u4e66\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u5206\u6790\u62a5\u544a\uff1a\u57fa\u4e8e10.5\u4e07\u6761\u6570\u636e\u6784\u5efa\u8bc4\u4f30\u6a21\u578b\uff0c\u8bc6\u522b\u9ad8\u5f71\u54cd\u529b\u5185\u5bb9\u7279\u5f81\uff0c\u4f18\u5316\u63a8\u8350\u7b97\u6cd5\u4e0e\u8fd0\u8425\u7b56\u7565\uff0c\u6db5\u76d6\u7528\u6237\u5206\u5c42\u3001\u4e92\u52a8\u6570\u636e\u3001\u5730\u7406\u4f4d\u7f6e\u5206\u5e03\uff0c\u63d0\u4f9b\u5185\u5bb9\u7b56\u7565\u4f18\u5316\u4e0e\u521b\u4f5c\u8005\u6210\u957f\u5efa\u8bae\u3002"},"content":{"rendered":"<p>\u62a5\u544a\u6807\u9898\uff1a\u5c0f\u7ea2\u4e66\u5185\u5bb9\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u4e0e\u5185\u5bb9\u7b56\u7565\u4f18\u5316\u5206\u6790<\/p>\n<p>\u4e00\u3001\u573a\u666f\u80cc\u666f\u4e0e\u4ef7\u503c<\/p>\n<p>\u4e1a\u52a1\u80cc\u666f \uff1a\u5c0f\u7ea2\u4e66\u4f5c\u4e3a\u4e2d\u56fd\u9886\u5148\u7684\u751f\u6d3b&#8230;<\/p>\n<h1>2025\u5e74\u5c0f\u7ea2\u4e66\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u5206\u6790\u62a5\u544a\uff1a\u57fa\u4e8e10.5\u4e07\u6761\u6570\u636e\u6784\u5efa\u8bc4\u4f30\u6a21\u578b\uff0c\u8bc6\u522b\u9ad8\u5f71\u54cd\u529b\u5185\u5bb9\u7279\u5f81\uff0c\u4f18\u5316\u63a8\u8350\u7b97\u6cd5\u4e0e\u8fd0\u8425\u7b56\u7565\uff0c\u6db5\u76d6\u7528\u6237\u5206\u5c42\u3001\u4e92\u52a8\u6570\u636e\u3001\u5730\u7406\u4f4d\u7f6e\u5206\u5e03\uff0c\u63d0\u4f9b\u5185\u5bb9\u7b56\u7565\u4f18\u5316\u4e0e\u521b\u4f5c\u8005\u6210\u957f\u5efa\u8bae\u3002<\/h1>\n<h3>\u62a5\u544a\u6807\u9898\uff1a\u5c0f\u7ea2\u4e66\u5185\u5bb9\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u4e0e\u5185\u5bb9\u7b56\u7565\u4f18\u5316\u5206\u6790<\/h3>\n<h4>\u4e00\u3001\u573a\u666f\u80cc\u666f\u4e0e\u4ef7\u503c<\/h4>\n<p><strong>\u4e1a\u52a1\u80cc\u666f<\/strong> \uff1a\u5c0f\u7ea2\u4e66\u4f5c\u4e3a\u4e2d\u56fd\u9886\u5148\u7684\u751f\u6d3b\u65b9\u5f0f\u5206\u4eab\u5e73\u53f0\uff0c\u62e5\u6709\u8d85\u8fc73\u4ebf\u7528\u6237\uff0c\u5176\u4e2d\u5185\u5bb9\u521b\u4f5c\u8005\u662f\u5e73\u53f0\u751f\u6001\u7684\u6838\u5fc3\u9a71\u52a8\u529b\u3002\u968f\u7740\u5e73\u53f0\u5546\u4e1a\u5316\u8fdb\u7a0b\u7684\u52a0\u901f\uff0c\u5982\u4f55\u8bc6\u522b\u9ad8\u5f71\u54cd\u529b\u521b\u4f5c\u8005\u3001\u4f18\u5316\u5185\u5bb9\u63a8\u8350\u7b97\u6cd5\u3001\u63d0\u5347\u7528\u6237\u7c98\u6027\u6210\u4e3a\u5e73\u53f0\u8fd0\u8425\u7684\u5173\u952e\u6311\u6218\u3002\u901a\u8fc7\u5206\u679010.5\u4e07\u6761\u771f\u5b9e\u5185\u5bb9\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u6df1\u5165\u7406\u89e3\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u5f62\u6210\u673a\u5236\uff0c\u4e3a\u5e73\u53f0\u8fd0\u8425\u548c\u521b\u4f5c\u8005\u6210\u957f\u63d0\u4f9b\u6570\u636e\u652f\u6491\u3002<\/p>\n<p><strong>\u672c\u62a5\u544a\u76ee\u6807<\/strong> \uff1a\u901a\u8fc7\u591a\u7ef4\u5ea6\u6570\u636e\u5206\u6790\uff0c\u6784\u5efa\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u8bc4\u4f30\u6a21\u578b\uff0c\u8bc6\u522b\u9ad8\u5f71\u54cd\u529b\u5185\u5bb9\u7684\u5173\u952e\u7279\u5f81\uff0c\u4e3a\u5185\u5bb9\u7b56\u7565\u4f18\u5316\u63d0\u4f9b\u79d1\u5b66\u4f9d\u636e\u548c\u53ef\u64cd\u4f5c\u5efa\u8bae\u3002<\/p>\n<h4>\u4e8c\u3001\u6570\u636e\u521d\u63a2\u4e0e\u9884\u5904\u7406<\/h4>\n<table>\n<thead>\n<tr>\n<th>\u6570\u636e\u96c6\u83b7\u53d6<\/th>\n<th><a href=\"https:\/\/dianshudata.com\/dataDetail\/13711\">10\u4e07\u6761\u5c0f\u7ea2\u4e66\u7b14\u8bb0\u94fe\u63a5<\/a><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>\u6570\u636e\u52a0\u8f7d\u4e0e\u6982\u89c8<\/strong> \uff1a\u672c\u6570\u636e\u96c6\u5305\u542b105,000\u6761\u5c0f\u7ea2\u4e66\u5185\u5bb9\u8bb0\u5f55\uff0c\u6bcf\u6761\u8bb0\u5f55\u5305\u542b25\u4e2a\u5b57\u6bb5\uff0c\u6db5\u76d6\u5185\u5bb9\u4fe1\u606f\u3001\u7528\u6237\u7279\u5f81\u3001\u5730\u7406\u4f4d\u7f6e\u3001\u60c5\u611f\u5206\u6790\u3001\u4e92\u52a8\u6570\u636e\u7b49\u591a\u4e2a\u7ef4\u5ea6\u3002\u6570\u636e\u65f6\u95f4\u8de8\u5ea6\u4ece2025\u5e749\u67081\u65e5\u52309\u670825\u65e5\uff0c\u8986\u76d6\u5168\u56fd35\u4e2a\u7701\u4efd\u3001323\u4e2a\u57ce\u5e02\u7684\u5185\u5bb9\u521b\u4f5c\u8005\u3002<\/p>\n<p><strong>\u6570\u636e\u6e05\u6d17\u8fc7\u7a0b<\/strong> \uff1a<\/p>\n<ol>\n<li><strong>\u7f3a\u5931\u503c\u5904\u7406<\/strong> \uff1a\u6570\u636e\u8d28\u91cf\u826f\u597d\uff0c\u65e0\u7f3a\u5931\u503c\uff0c\u6240\u6709\u5b57\u6bb5\u5b8c\u6574\u5ea6100%<\/li>\n<li><strong>\u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/strong> \uff1a\u5c06\u7c89\u4e1d\u6570\u3001\u4e92\u52a8\u6570\u636e\u7b49\u6570\u503c\u5b57\u6bb5\u8f6c\u6362\u4e3a\u6b63\u786e\u7684\u6570\u503c\u7c7b\u578b<\/li>\n<li><strong>\u5f02\u5e38\u503c\u5904\u7406<\/strong> \uff1a\u53d1\u73b0\u7c89\u4e1d\u6570\u5b58\u5728\u6781\u7aef\u5f02\u5e38\u503c\uff08\u6700\u592717\u4ebf\uff09\uff0c\u901a\u8fc7\u5206\u5c42\u5206\u6790\u5904\u7406<\/li>\n<li><strong>\u7279\u5f81\u5de5\u7a0b<\/strong> \uff1a\u521b\u5efa\u7528\u6237\u5f71\u54cd\u529b\u5206\u5c42\u3001\u5185\u5bb9\u8d28\u91cf\u8bc4\u5206\u3001\u7528\u6237\u6d3b\u8dc3\u5ea6\u7b49\u884d\u751f\u7279\u5f81<\/li>\n<\/ol>\n<p><strong>\u6570\u636e\u8d28\u91cf\u8bc4\u4f30<\/strong> \uff1a<\/p>\n<ul>\n<li>\u5e73\u5747\u7c89\u4e1d\u6570\uff1a16\u4eba\uff08\u4e2d\u4f4d\u657010\u4eba\uff0c\u5b58\u5728\u660e\u663e\u957f\u5c3e\u5206\u5e03\uff09<\/li>\n<li>\u5e73\u5747\u5185\u5bb9\u957f\u5ea6\uff1a216.3\u5b57\u7b26<\/li>\n<li>\u5e73\u5747\u6807\u7b7e\u6570\uff1a7.7\u4e2a<\/li>\n<li>\u5e73\u5747\u603b\u4e92\u52a8\u6570\uff1a1.87\u6b21<\/li>\n<\/ul>\n<h4>\u4e09\u3001\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\uff08EDA\uff09\u4e0e\u6838\u5fc3\u6d1e\u5bdf<\/h4>\n<p><strong>\u7528\u6237\u5f71\u54cd\u529b\u5206\u5c42\u5206\u6790<\/strong> \uff1a<br \/>\n\u901a\u8fc7\u7c89\u4e1d\u6570\u5206\u5c42\u53d1\u73b0\uff0c\u5e73\u53f0\u7528\u6237\u5448\u73b0\u660e\u663e\u7684\u91d1\u5b57\u5854\u7ed3\u6784\uff1a<\/p>\n<ul>\n<li>\u65b0\u624b\u521b\u4f5c\u8005\uff080-100\u7c89\u4e1d\uff09\uff1a72,133\u4eba\uff0868.7%\uff09<\/li>\n<li>\u6210\u957f\u521b\u4f5c\u8005\uff08100-1K\u7c89\u4e1d\uff09\uff1a13,309\u4eba\uff0812.7%\uff09<\/li>\n<li>\u6210\u719f\u521b\u4f5c\u8005\uff081K-10K\u7c89\u4e1d\uff09\uff1a3,910\u4eba\uff083.7%\uff09<\/li>\n<li>\u5927V\u521b\u4f5c\u8005\uff0810K-100K\u7c89\u4e1d\uff09\uff1a9\u4eba\uff080.01%\uff09<\/li>\n<li>\u8d85\u7ea7\u5927V\uff08100K+\u7c89\u4e1d\uff09\uff1a16\u4eba\uff080.02%\uff09<\/li>\n<li><img decoding=\"async\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" src=\"https:\/\/wp.dianshudata.com\/story\/wp-content\/uploads\/2026\/05\/img-fd7bf405.png\" \/><\/li>\n<\/ul>\n<p><img decoding=\"async\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" src=\"https:\/\/wp.dianshudata.com\/story\/wp-content\/uploads\/2026\/05\/img-c4d7851c.png\" \/><\/p>\n<p><strong>\u5185\u5bb9\u4e92\u52a8\u7279\u5f81\u5206\u6790<\/strong> \uff1a<br \/>\n\u4e92\u52a8\u6570\u636e\u5448\u73b0\u6781\u5ea6\u4e0d\u5747\u8861\u5206\u5e03\uff1a<\/p>\n<ul>\n<li>\u70b9\u8d5e\u6570\uff1a\u5e73\u57471.23\u6b21\uff0c\u4e2d\u4f4d\u65700\u6b21\uff0c\u6700\u592737,787\u6b21<\/li>\n<li>\u5206\u4eab\u6570\uff1a\u5e73\u57470.15\u6b21\uff0c\u4e2d\u4f4d\u65700\u6b21\uff0c\u6700\u59273,167\u6b21<\/li>\n<li>\u6536\u85cf\u6570\uff1a\u5e73\u57470.35\u6b21\uff0c\u4e2d\u4f4d\u65700\u6b21\uff0c\u6700\u592710,521\u6b21<\/li>\n<li>\u8bc4\u8bba\u6570\uff1a\u5e73\u57470.14\u6b21\uff0c\u4e2d\u4f4d\u65700\u6b21\uff0c\u6700\u59275,374\u6b21<\/li>\n<\/ul>\n<p><strong>\u5730\u7406\u4f4d\u7f6e\u5206\u5e03\u6d1e\u5bdf<\/strong> \uff1a<br \/>\n\u5185\u5bb9\u521b\u4f5c\u5448\u73b0\u660e\u663e\u7684\u5730\u57df\u96c6\u4e2d\u7279\u5f81\uff1a<\/p>\n<ul>\n<li>\u5e7f\u4e1c\u7701\uff1a5,547\u6761\uff085.3%\uff09\uff0c\u5185\u5bb9\u521b\u4f5c\u6700\u6d3b\u8dc3<\/li>\n<li>\u5317\u4eac\u5e02\uff1a3,031\u6761\uff082.9%\uff09\uff0c\u9ad8\u8d28\u91cf\u5185\u5bb9\u96c6\u4e2d<\/li>\n<li>\u6c5f\u82cf\u7701\uff1a3,006\u6761\uff082.9%\uff09\uff0c\u5185\u5bb9\u591a\u6837\u6027\u4e30\u5bcc<\/li>\n<li>\u4e00\u7ebf\u57ce\u5e02\uff08\u5317\u4eac\u3001\u4e0a\u6d77\u3001\u6df1\u5733\u3001\u5e7f\u5dde\uff09\u5360\u603b\u5185\u5bb9\u768412.1%<br \/>\n<img decoding=\"async\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" src=\"https:\/\/wp.dianshudata.com\/story\/wp-content\/uploads\/2026\/05\/img-21093503.png\" \/><\/li>\n<\/ul>\n<p><strong>\u5185\u5bb9\u4e3b\u9898\u5206\u6790<\/strong> \uff1a<br \/>\n\u901a\u8fc7\u6807\u7b7e\u5206\u6790\u53d1\u73b0\u70ed\u95e8\u5185\u5bb9\u4e3b\u9898\uff1a<\/p>\n<ul>\n<li>#\u7f8e\u98df\uff1a3,732\u6b21\uff0c\u6700\u53d7\u6b22\u8fce\u7684\u5185\u5bb9\u7c7b\u578b<\/li>\n<li>#\u65c5\u6e38\uff1a3,345\u6b21\uff0c\u751f\u6d3b\u65b9\u5f0f\u5206\u4eab\u6838\u5fc3<\/li>\n<li>#\u65c5\u6e38\u4e07\u7c89\u6276\u6301\u8ba1\u5212\uff1a2,508\u6b21\uff0c\u5e73\u53f0\u6d3b\u52a8\u53c2\u4e0e\u5ea6\u9ad8<\/li>\n<li>#\u7a7f\u642d\u76f8\u5173\u6807\u7b7e\uff1a\u5360\u636e\u524d20\u4e2d\u76848\u4e2a\u4f4d\u7f6e<\/li>\n<\/ul>\n<p><strong>\u60c5\u611f\u5206\u6790\u7ed3\u679c<\/strong> \uff1a<\/p>\n<ul>\n<li>\u4e2d\u6027\u60c5\u611f\uff080\uff09\uff1a99,421\u6761\uff0894.7%\uff09\uff0c\u5185\u5bb9\u6574\u4f53\u79ef\u6781\u6b63\u9762<\/li>\n<li>\u6b63\u9762\u60c5\u611f\uff081-2\uff09\uff1a3,571\u6761\uff083.4%\uff09<\/li>\n<li>\u8d1f\u9762\u60c5\u611f\uff08-1\u81f3-6\uff09\uff1a1,008\u6761\uff081.9%\uff09<\/li>\n<\/ul>\n<h4>\u56db\u3001\u573a\u666f\u5efa\u6a21\u4e0e\u5b9e\u73b0<\/h4>\n<p><strong>\u6a21\u578b\u9009\u62e9\u4e0e\u539f\u7406<\/strong> \uff1a<br \/>\n\u9009\u62e9\u968f\u673a\u68ee\u6797\u56de\u5f52\u6a21\u578b\u8fdb\u884c\u5f71\u54cd\u529b\u9884\u6d4b\uff0c\u539f\u56e0\u5982\u4e0b\uff1a<\/p>\n<ol>\n<li><strong>\u975e\u7ebf\u6027\u5173\u7cfb\u5904\u7406<\/strong> \uff1a\u80fd\u591f\u6355\u6349\u7279\u5f81\u95f4\u7684\u590d\u6742\u4ea4\u4e92\u5173\u7cfb<\/li>\n<li><strong>\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790<\/strong> \uff1a\u63d0\u4f9b\u53ef\u89e3\u91ca\u7684\u7279\u5f81\u6743\u91cd<\/li>\n<li><strong>\u9c81\u68d2\u6027\u5f3a<\/strong> \uff1a\u5bf9\u5f02\u5e38\u503c\u548c\u566a\u58f0\u6570\u636e\u4e0d\u654f\u611f<\/li>\n<li><strong>\u5904\u7406\u80fd\u529b\u5f3a<\/strong> \uff1a\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6<\/li>\n<\/ol>\n<p><strong>\u7279\u5f81\u5de5\u7a0b\u7b56\u7565<\/strong> \uff1a<br \/>\n\u6784\u5efa11\u4e2a\u6838\u5fc3\u7279\u5f81\uff1a<\/p>\n<ul>\n<li>\u7528\u6237\u7279\u5f81\uff1a\u7c89\u4e1d\u6570\u3001\u5173\u6ce8\u6570\u3001\u8ba4\u8bc1\u72b6\u6001\u3001\u5f71\u54cd\u529b\u5206\u5c42<\/li>\n<li>\u5185\u5bb9\u7279\u5f81\uff1a\u6807\u9898\u957f\u5ea6\u3001\u5185\u5bb9\u957f\u5ea6\u3001\u6807\u7b7e\u6570\u91cf\u3001\u5185\u5bb9\u8d28\u91cf\u8bc4\u5206<\/li>\n<li>\u884c\u4e3a\u7279\u5f81\uff1a\u7528\u6237\u6d3b\u8dc3\u5ea6\u8bc4\u5206\u3001\u60c5\u611f\u503e\u5411<\/li>\n<\/ul>\n<p><strong>\u6a21\u578b\u5b9e\u73b0\u4e0e\u8bc4\u4f30<\/strong> \uff1a<\/p>\n<ul>\n<li>\u8bad\u7ec3\u96c6\uff1a84,009\u6761\u8bb0\u5f55\uff0880%\uff09<\/li>\n<li>\u6d4b\u8bd5\u96c6\uff1a21,003\u6761\u8bb0\u5f55\uff0820%\uff09<\/li>\n<\/ul>\n<p><strong>\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790<\/strong> \uff1a<\/p>\n<ol>\n<li>\u5185\u5bb9\u8d28\u91cf\u8bc4\u5206\uff1a43.63%\uff08\u6700\u91cd\u8981\uff09<\/li>\n<li>\u6807\u7b7e\u6570\u91cf\uff1a14.30%<\/li>\n<li>\u6807\u7b7e\u6570\u91cf\uff1a12.65%<\/li>\n<li>\u6807\u9898\u957f\u5ea6\uff1a11.90%<\/li>\n<li>\u5185\u5bb9\u957f\u5ea6\uff1a9.39%<\/li>\n<\/ol>\n<p><img decoding=\"async\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" src=\"https:\/\/wp.dianshudata.com\/story\/wp-content\/uploads\/2026\/05\/img-206f5593.png\" \/><\/p>\n<h4>\u4e94\u3001\u5b8c\u6574\u4ee3\u7801\u5b9e\u73b0\uff08\u5e26\u8be6\u7ec6\u6ce8\u91ca\uff09<\/h4>\n<pre><code># -*- coding: utf-8 -*-\n\"\"\"\n\u5c0f\u7ea2\u4e66\u5185\u5bb9\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u5206\u6790\u5b8c\u6574\u4ee3\u7801\n\u4f5c\u8005\uff1a\u6570\u636e\u5206\u6790\u4e13\u5bb6\n\u65e5\u671f\uff1a2025\u5e741\u6708\n\"\"\"\n\nimport json\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom collections import Counter\nimport re\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom sklearn.preprocessing import StandardScaler\n\n# \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53\nplt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei']\nplt.rcParams['axes.unicode_minus'] = False\n\ndef load_and_clean_data(file_path):\n    \"\"\"\n    \u52a0\u8f7d\u548c\u6e05\u6d17\u5c0f\u7ea2\u4e66\u6570\u636e\n\n    Args:\n        file_path: JSON\u6570\u636e\u6587\u4ef6\u8def\u5f84\n\n    Returns:\n        pd.DataFrame: \u6e05\u6d17\u540e\u7684\u6570\u636e\u6846\n    \"\"\"\n    print(\"=== \u5f00\u59cb\u6570\u636e\u52a0\u8f7d\u548c\u6e05\u6d17 ===\")\n\n    # \u8bfb\u53d6\u539f\u59cb\u6570\u636e\n    data = []\n    with open(file_path, 'r', encoding='utf-8') as f:\n        for line in f:\n            try:\n                data.append(json.loads(line.strip()))\n            except:\n                continue\n\n    print(f\"\u6210\u529f\u8bfb\u53d6 {len(data)} \u6761\u539f\u59cb\u6570\u636e\")\n\n    # \u6570\u636e\u6e05\u6d17\u548c\u7279\u5f81\u63d0\u53d6\n    cleaned_data = []\n\n    for i, record in enumerate(data):\n        try:\n            if 'data' not in record:\n                continue\n\n            data_info = record['data']\n\n            # \u63d0\u53d6\u57fa\u7840\u4fe1\u606f\n            record_id = record.get('id', '')\n            title = data_info.get('title', '')\n            content = data_info.get('content', '')\n\n            # \u63d0\u53d6\u7528\u6237\u4fe1\u606f\n            user_info = data_info.get('user', {})\n            user_name = user_info.get('name', '')\n            user_gender = user_info.get('gender', '')\n            followers_count = user_info.get('followers_count', 0)\n            friends_count = user_info.get('friends_count', 0)\n            verified = user_info.get('verified', 0)\n\n            # \u63d0\u53d6\u5730\u7406\u4f4d\u7f6e\u4fe1\u606f\n            analysis = data_info.get('analysis', {})\n            find_address = analysis.get('find_address', {})\n            province = find_address.get('province', [])\n            city = find_address.get('city', [])\n\n            # \u63d0\u53d6\u60c5\u611f\u5206\u6790\n            sentiment = analysis.get('sentiment', 0)\n\n            # \u63d0\u53d6\u6807\u7b7e\u4fe1\u606f\n            tags = analysis.get('tag', [])\n            tag_count = len(tags)\n\n            # \u63d0\u53d6\u65f6\u95f4\u4fe1\u606f\n            ctime = data_info.get('ctime', 0)\n            utime = data_info.get('utime', 0)\n\n            # \u63d0\u53d6\u4e92\u52a8\u6570\u636e\n            like_count = data_info.get('like_count', 0)\n            share_count = data_info.get('share_count', 0)\n            collection_count = data_info.get('collection_count', 0)\n            reply_count = data_info.get('reply_count', 0)\n            visit_count = data_info.get('visit_count', 0)\n\n            # \u8ba1\u7b97\u5185\u5bb9\u957f\u5ea6\n            title_length = len(title) if title else 0\n            content_length = len(content) if content else 0\n\n            # \u63d0\u53d6\u6807\u7b7e\u6587\u672c\n            hashtags = re.findall(r'#([^#\\\\s]+)', content)\n            hashtag_count = len(hashtags)\n\n            # \u8ba1\u7b97\u5f71\u54cd\u529b\u6307\u6807\n            total_interaction = like_count + share_count + collection_count + reply_count\n\n            cleaned_record = {\n                'id': record_id,\n                'title': title,\n                'content': content,\n                'user_name': user_name,\n                'user_gender': user_gender,\n                'followers_count': followers_count,\n                'friends_count': friends_count,\n                'verified': verified,\n                'province': province[0] if province else '',\n                'city': city[0] if city else '',\n                'sentiment': sentiment,\n                'tag_count': tag_count,\n                'ctime': ctime,\n                'utime': utime,\n                'like_count': like_count,\n                'share_count': share_count,\n                'collection_count': collection_count,\n                'reply_count': reply_count,\n                'visit_count': visit_count,\n                'title_length': title_length,\n                'content_length': content_length,\n                'hashtag_count': hashtag_count,\n                'total_interaction': total_interaction,\n                'hashtags': hashtags\n            }\n\n            cleaned_data.append(cleaned_record)\n\n        except Exception as e:\n            if i &lt; 10:  # \u53ea\u6253\u5370\u524d10\u4e2a\u9519\u8bef\n                print(f'\u5904\u7406\u7b2c{i}\u6761\u8bb0\u5f55\u65f6\u51fa\u9519: {e}')\n            continue\n\n    print(f\"\u6210\u529f\u5904\u7406 {len(cleaned_data)} \u6761\u6570\u636e\")\n\n    # \u8f6c\u6362\u4e3aDataFrame\n    df = pd.DataFrame(cleaned_data)\n\n    # \u8f6c\u6362\u6570\u503c\u5217\n    numeric_cols = ['followers_count', 'friends_count', 'verified', 'like_count', \n                    'share_count', 'collection_count', 'reply_count', 'visit_count',\n                    'title_length', 'content_length', 'hashtag_count', 'total_interaction', 'sentiment', 'tag_count']\n\n    for col in numeric_cols:\n        if col in df.columns:\n            df[col] = pd.to_numeric(df[col], errors='coerce')\n\n    return df\n\ndef create_visualizations(df):\n    \"\"\"\n    \u521b\u5efa\u6570\u636e\u53ef\u89c6\u5316\u56fe\u8868\n\n    Args:\n        df: \u6e05\u6d17\u540e\u7684\u6570\u636e\u6846\n    \"\"\"\n    print(\"=== \u521b\u5efa\u53ef\u89c6\u5316\u56fe\u8868 ===\")\n\n    # \u521b\u5efa\u56fe\u8868\n    fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n    fig.suptitle('\u5c0f\u7ea2\u4e66\u5185\u5bb9\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u5206\u6790 - \u6570\u636e\u53ef\u89c6\u5316', fontsize=16, fontweight='bold')\n\n    # 1. \u7528\u6237\u7c89\u4e1d\u6570\u5206\u5e03\uff08\u5bf9\u6570\u5c3a\u5ea6\uff09\n    ax1 = axes[0, 0]\n    followers_clean = df[df['followers_count'] &lt; 1000000]['followers_count']\n    ax1.hist(np.log10(followers_clean + 1), bins=50, alpha=0.7, color='skyblue', edgecolor='black')\n    ax1.set_xlabel('\u7c89\u4e1d\u6570\uff08\u5bf9\u6570\u5c3a\u5ea6\uff09')\n    ax1.set_ylabel('\u9891\u6b21')\n    ax1.set_title('\u7528\u6237\u7c89\u4e1d\u6570\u5206\u5e03')\n    ax1.set_xticks([0, 1, 2, 3, 4, 5, 6])\n    ax1.set_xticklabels(['1', '10', '100', '1K', '10K', '100K', '1M'])\n\n    # 2. \u5185\u5bb9\u4e92\u52a8\u6570\u636e\u5206\u5e03\n    ax2 = axes[0, 1]\n    interaction_data = [df['like_count'], df['share_count'], df['collection_count'], df['reply_count']]\n    interaction_labels = ['\u70b9\u8d5e', '\u5206\u4eab', '\u6536\u85cf', '\u8bc4\u8bba']\n    ax2.boxplot(interaction_data, labels=interaction_labels)\n    ax2.set_ylabel('\u4e92\u52a8\u6570\u91cf')\n    ax2.set_title('\u5185\u5bb9\u4e92\u52a8\u6570\u636e\u5206\u5e03')\n    ax2.set_yscale('log')\n\n    # 3. \u7528\u6237\u5206\u5c42\u5206\u5e03\n    ax3 = axes[0, 2]\n    df['follower_tier'] = pd.cut(df['followers_count'], \n                                bins=[0, 100, 1000, 10000, 100000, float('inf')],\n                                labels=['\u65b0\u624b(0-100)', '\u6210\u957f(100-1K)', '\u6210\u719f(1K-10K)', '\u5927V(10K-100K)', '\u8d85\u7ea7\u5927V(100K+)'])\n    tier_counts = df['follower_tier'].value_counts()\n    colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#ff99cc']\n    wedges, texts, autotexts = ax3.pie(tier_counts.values, labels=tier_counts.index, autopct='%1.1f%%', \n                                       colors=colors, startangle=90)\n    ax3.set_title('\u7528\u6237\u5f71\u54cd\u529b\u5206\u5c42\u5206\u5e03')\n\n    # 4. \u5730\u7406\u4f4d\u7f6e\u5206\u5e03\uff08TOP10\u7701\u4efd\uff09\n    ax4 = axes[1, 0]\n    province_counts = df['province'].value_counts().head(10)\n    bars = ax4.barh(range(len(province_counts)), province_counts.values, color='lightcoral')\n    ax4.set_yticks(range(len(province_counts)))\n    ax4.set_yticklabels(province_counts.index)\n    ax4.set_xlabel('\u5185\u5bb9\u6570\u91cf')\n    ax4.set_title('\u7701\u4efd\u5185\u5bb9\u5206\u5e03TOP10')\n    ax4.invert_yaxis()\n\n    # 5. \u5185\u5bb9\u957f\u5ea6\u4e0e\u4e92\u52a8\u5173\u7cfb\n    ax5 = axes[1, 1]\n    sample_df = df.sample(n=min(10000, len(df)))\n    ax5.scatter(sample_df['content_length'], sample_df['total_interaction'], \n               alpha=0.5, s=20, color='purple')\n    ax5.set_xlabel('\u5185\u5bb9\u957f\u5ea6\uff08\u5b57\u7b26\uff09')\n    ax5.set_ylabel('\u603b\u4e92\u52a8\u6570')\n    ax5.set_title('\u5185\u5bb9\u957f\u5ea6\u4e0e\u4e92\u52a8\u5173\u7cfb')\n    ax5.set_yscale('log')\n\n    # 6. \u53d1\u5e03\u65f6\u95f4\u5206\u5e03\n    ax6 = axes[1, 2]\n    df['create_time'] = pd.to_datetime(df['ctime'], unit='s')\n    df['hour'] = df['create_time'].dt.hour\n    hour_distribution = df['hour'].value_counts().sort_index()\n    ax6.plot(hour_distribution.index, hour_distribution.values, marker='o', linewidth=2, markersize=6)\n    ax6.set_xlabel('\u53d1\u5e03\u65f6\u95f4\uff08\u5c0f\u65f6\uff09')\n    ax6.set_ylabel('\u5185\u5bb9\u6570\u91cf')\n    ax6.set_title('\u5185\u5bb9\u53d1\u5e03\u65f6\u95f4\u5206\u5e03')\n    ax6.set_xticks(range(0, 24, 2))\n    ax6.grid(True, alpha=0.3)\n\n    plt.tight_layout()\n    plt.savefig('xiaohongshu_analysis_charts.png', dpi=300, bbox_inches='tight')\n    plt.show()\n\n    print(\"\u56fe\u8868\u5df2\u4fdd\u5b58\u4e3a xiaohongshu_analysis_charts.png\")\n\ndef build_influence_model(df):\n    \"\"\"\n    \u6784\u5efa\u5f71\u54cd\u529b\u9884\u6d4b\u6a21\u578b\n\n    Args:\n        df: \u6e05\u6d17\u540e\u7684\u6570\u636e\u6846\n\n    Returns:\n        tuple: (\u6a21\u578b, \u7279\u5f81\u91cd\u8981\u6027, \u8bc4\u4f30\u6307\u6807)\n    \"\"\"\n    print(\"=== \u6784\u5efa\u5f71\u54cd\u529b\u9884\u6d4b\u6a21\u578b ===\")\n\n    # \u7279\u5f81\u5de5\u7a0b\n    df['follower_tier'] = pd.cut(df['followers_count'], \n                                bins=[0, 100, 1000, 10000, 100000, float('inf')],\n                                labels=[0, 1, 2, 3, 4])\n\n    # \u5185\u5bb9\u8d28\u91cf\u6307\u6807\n    df['content_quality_score'] = (df['title_length'] * 0.3 + \n                                  df['content_length'] * 0.4 + \n                                  df['hashtag_count'] * 0.3)\n\n    # \u7528\u6237\u6d3b\u8dc3\u5ea6\u6307\u6807\n    df['user_activity_score'] = (df['friends_count'] * 0.3 + \n                                df['followers_count'] * 0.7)\n\n    # \u9009\u62e9\u7279\u5f81\n    feature_cols = [\n        'followers_count', 'friends_count', 'verified', 'follower_tier',\n        'title_length', 'content_length', 'hashtag_count', 'tag_count',\n        'content_quality_score', 'user_activity_score', 'sentiment'\n    ]\n\n    target_col = 'total_interaction'\n\n    # \u51c6\u5907\u6570\u636e\n    X = df[feature_cols].fillna(0)\n    y = df[target_col].fillna(0)\n\n    # \u6570\u636e\u5206\u5272\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n    # \u8bad\u7ec3\u968f\u673a\u68ee\u6797\u6a21\u578b\n    rf_model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=-1)\n    rf_model.fit(X_train, y_train)\n    rf_pred = rf_model.predict(X_test)\n    rf_r2 = r2_score(y_test, rf_pred)\n    rf_rmse = np.sqrt(mean_squared_error(y_test, rf_pred))\n\n    # \u7279\u5f81\u91cd\u8981\u6027\n    feature_importance = pd.DataFrame({\n        'feature': feature_cols,\n        'importance': rf_model.feature_importances_\n    }).sort_values('importance', ascending=False)\n\n    print(f\"\u968f\u673a\u68ee\u6797\u6a21\u578b - R\u00b2: {rf_r2:.4f}, RMSE: {rf_rmse:.4f}\")\n\n    return rf_model, feature_importance, (rf_r2, rf_rmse)\n\ndef main():\n    \"\"\"\n    \u4e3b\u51fd\u6570\uff1a\u6267\u884c\u5b8c\u6574\u7684\u6570\u636e\u5206\u6790\u6d41\u7a0b\n    \"\"\"\n    print(\"\u5f00\u59cb\u5c0f\u7ea2\u4e66\u5185\u5bb9\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u5206\u6790\")\n\n    # 1. \u6570\u636e\u52a0\u8f7d\u548c\u6e05\u6d17\n    df = load_and_clean_data('xiaohongshu.json')\n\n    # 2. \u4fdd\u5b58\u6e05\u6d17\u540e\u7684\u6570\u636e\n    df.to_csv('xiaohongshu_cleaned.csv', index=False, encoding='utf-8')\n    print(\"\u6e05\u6d17\u540e\u7684\u6570\u636e\u5df2\u4fdd\u5b58\u5230 xiaohongshu_cleaned.csv\")\n\n    # 3. \u521b\u5efa\u53ef\u89c6\u5316\n    create_visualizations(df)\n\n    # 4. \u6784\u5efa\u6a21\u578b\n    model, feature_importance, metrics = build_influence_model(df)\n\n    # 5. \u8f93\u51fa\u7ed3\u679c\n    print(\"\\\\n=== \u5206\u6790\u5b8c\u6210 ===\")\n    print(f\"\u6570\u636e\u91cf: {len(df)} \u6761\")\n    print(f\"\u6a21\u578bR\u00b2: {metrics[0]:.4f}\")\n    print(f\"\u6a21\u578bRMSE: {metrics[1]:.4f}\")\n    print(\"\\\\n\u7279\u5f81\u91cd\u8981\u6027TOP5:\")\n    for idx, row in feature_importance.head().iterrows():\n        print(f\"{row['feature']}: {row['importance']:.4f}\")\n\nif __name__ == \"__main__\":\n    main()\n<\/code><\/pre>\n<h4>\u516d\u3001\u603b\u7ed3\u4e0e\u4e1a\u52a1\u5efa\u8bae<\/h4>\n<p><strong>\u5206\u6790\u603b\u7ed3<\/strong> \uff1a<br \/>\n\u901a\u8fc7\u5bf910.5\u4e07\u6761\u5c0f\u7ea2\u4e66\u5185\u5bb9\u7684\u6df1\u5ea6\u5206\u6790\uff0c\u6211\u4eec\u53d1\u73b0\u4e86\u51e0\u4e2a\u5173\u952e\u6d1e\u5bdf\uff1a1\uff09\u5e73\u53f0\u7528\u6237\u5448\u73b0\u660e\u663e\u7684\u91d1\u5b57\u5854\u7ed3\u6784\uff0c68.7%\u4e3a\u65b0\u624b\u521b\u4f5c\u8005\uff1b2\uff09\u5185\u5bb9\u4e92\u52a8\u5448\u73b0\u6781\u5ea6\u4e0d\u5747\u8861\u5206\u5e03\uff0c\u5927\u90e8\u5206\u5185\u5bb9\u4e92\u52a8\u6570\u4e3a0\uff1b3\uff09\u5185\u5bb9\u8d28\u91cf\u8bc4\u5206\u662f\u5f71\u54cd\u4e92\u52a8\u7684\u6700\u91cd\u8981\u56e0\u7d20\uff0843.63%\uff09\uff1b4\uff09\u7f8e\u98df\u548c\u65c5\u6e38\u662f\u5e73\u53f0\u6700\u53d7\u6b22\u8fce\u7684\u5185\u5bb9\u7c7b\u578b\uff1b5\uff09\u4e00\u7ebf\u57ce\u5e02\u521b\u4f5c\u8005\u8d21\u732e\u4e8612.1%\u7684\u5185\u5bb9\u3002<\/p>\n<p><strong>\u4e1a\u52a1\u5efa\u8bae<\/strong> \uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u5e73\u53f0\u8fd0\u8425\u4f18\u5316<\/strong> \uff1a\u5efa\u7acb\u5206\u5c42\u6fc0\u52b1\u673a\u5236\uff0c\u4e3a\u4e0d\u540c\u5c42\u7ea7\u521b\u4f5c\u8005\u63d0\u4f9b\u5dee\u5f02\u5316\u652f\u6301\uff1b\u4f18\u5316\u63a8\u8350\u7b97\u6cd5\uff0c\u91cd\u70b9\u5173\u6ce8\u5185\u5bb9\u8d28\u91cf\u8bc4\u5206\uff1b\u52a0\u5f3a\u5730\u57df\u5185\u5bb9\u5e73\u8861\uff0c\u9f13\u52b1\u4e8c\u4e09\u7ebf\u57ce\u5e02\u521b\u4f5c\u8005\u53c2\u4e0e\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u521b\u4f5c\u8005\u6210\u957f\u7b56\u7565<\/strong> \uff1a\u91cd\u70b9\u63d0\u5347\u5185\u5bb9\u8d28\u91cf\uff0c\u5305\u62ec\u6807\u9898\u5438\u5f15\u529b\u3001\u5185\u5bb9\u6df1\u5ea6\u548c\u6807\u7b7e\u4f7f\u7528\uff1b\u5efa\u8bae\u521b\u4f5c\u8005\u5173\u6ce8\u7f8e\u98df\u3001\u65c5\u6e38\u7b49\u70ed\u95e8\u9886\u57df\uff1b\u5408\u7406\u4f7f\u7528\u6807\u7b7e\u63d0\u5347\u5185\u5bb9\u66dd\u5149\u5ea6\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5546\u4e1a\u5316\u5efa\u8bae<\/strong> \uff1a\u54c1\u724c\u65b9\u5e94\u91cd\u70b9\u5173\u6ce8\u5185\u5bb9\u8d28\u91cf\u8bc4\u5206\u9ad8\u7684\u521b\u4f5c\u8005\uff1b\u4f18\u5148\u9009\u62e9\u4e00\u7ebf\u57ce\u5e02\u548c\u70ed\u95e8\u7701\u4efd\u7684\u521b\u4f5c\u8005\u8fdb\u884c\u5408\u4f5c\uff1b\u5173\u6ce8\u60c5\u611f\u503e\u5411\u79ef\u6781\u7684\u5185\u5bb9\u521b\u4f5c\u8005\u3002<\/p>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>2025\u5e74\u5c0f\u7ea2\u4e66\u521b\u4f5c\u8005\u5f71\u54cd\u529b\u5206\u6790\u62a5\u544a\uff1a\u57fa\u4e8e10.5\u4e07\u6761\u6570\u636e\u6784\u5efa\u8bc4\u4f30\u6a21\u578b\uff0c\u8bc6\u522b\u9ad8\u5f71\u54cd\u529b\u5185\u5bb9\u7279\u5f81\uff0c\u4f18\u5316\u63a8\u8350\u7b97\u6cd5\u4e0e\u8fd0\u8425\u7b56\u7565\uff0c\u6db5\u76d6\u7528\u6237\u5206\u5c42\u3001\u4e92\u52a8\u6570\u636e\u3001\u5730\u7406\u4f4d\u7f6e\u5206\u5e03\uff0c\u63d0\u4f9b\u5185\u5bb9\u7b56\u7565\u4f18\u5316\u4e0e\u521b\u4f5c\u8005\u6210\u957f\u5efa\u8bae\u3002<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"none","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[51],"tags":[198,35,199,200],"class_list":["post-2045","post","type-post","status-publish","format-standard","hentry","category-51","tag-198","tag-35","tag-199","tag-200"],"_links":{"self":[{"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/posts\/2045","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/comments?post=2045"}],"version-history":[{"count":1,"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/posts\/2045\/revisions"}],"predecessor-version":[{"id":2046,"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/posts\/2045\/revisions\/2046"}],"wp:attachment":[{"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/media?parent=2045"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/categories?post=2045"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dianshudata.com\/story\/wp-json\/wp\/v2\/tags?post=2045"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}