{"id":2902,"date":"2025-11-05T07:58:00","date_gmt":"2025-11-04T23:58:00","guid":{"rendered":"https:\/\/www.laoyulaoyu.com\/?p=2902"},"modified":"2025-10-28T14:36:12","modified_gmt":"2025-10-28T06:36:12","slug":"%e9%87%8f%e5%8c%96%e6%8a%95%e8%b5%84%e5%ae%9e%e6%88%98%ef%bc%9a6%e6%ad%a5%e6%95%99%e4%bd%a0%e7%94%a8%e3%80%90%e7%9c%9f%e5%ae%9e%e6%95%b0%e6%8d%aeai%e6%a8%a1%e5%9e%8b%e3%80%91%e8%b7%91%e8%b5%a2","status":"publish","type":"post","link":"https:\/\/www.laoyulaoyu.com\/index.php\/2025\/11\/05\/%e9%87%8f%e5%8c%96%e6%8a%95%e8%b5%84%e5%ae%9e%e6%88%98%ef%bc%9a6%e6%ad%a5%e6%95%99%e4%bd%a0%e7%94%a8%e3%80%90%e7%9c%9f%e5%ae%9e%e6%95%b0%e6%8d%aeai%e6%a8%a1%e5%9e%8b%e3%80%91%e8%b7%91%e8%b5%a2\/","title":{"rendered":"\u91cf\u5316\u6295\u8d44\u5b9e\u6218\uff1a6\u6b65\u6559\u4f60\u7528\u3010\u771f\u5b9e\u6570\u636e+AI\u6a21\u578b\u3011\u8dd1\u8d62\u5927\u76d8"},"content":{"rendered":"\n

\u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n

\u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002<\/mark><\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\n
\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u6700\u8fd1\u6211\u7528\u68af\u5ea6\u63d0\u5347\u7b97\u6cd5\u642d\u5efa\u4e86\u80a1\u4ef7\u9884\u6d4b\u6a21\u578b\uff0c\u53d1\u73b0\u6bd4\u4f20\u7edf\u65b9\u6cd5\u7cbe\u51c6\u5f97\u591a\uff0c\u56de\u6d4b5\u5e74\uff0c\u5e74\u5316\u6536\u76ca21.7%\uff0c\u80dc\u738763%\u3002<\/mark>\u4eca\u5929\u5c31\u628a\u4ece\u6570\u636e\u83b7\u53d6\u5230\u6a21\u578b\u90e8\u7f72\u7684\u5b8c\u6574\u6d41\u7a0b\u624b\u628a\u624b\u6559\u4f60\u590d\u73b0\uff01<\/pre>\n<\/blockquote>\n\n\n\n
\u4e00\u3001\u4ec0\u4e48\u662fGBR\uff1f<\/strong><\/strong><\/h2>\n\n\n\n
\u5f88\u591a\u4eba\u7092\u80a1\uff0c\u9760\u7684\u662f\u201c\u611f\u89c9\u201d\uff1a\u201c\u8fd9\u53ea\u80a1\u7968\u6700\u8fd1\u6da8\u5f97\u731b\uff0c\u5e94\u8be5\u8fd8\u80fd\u6da8\uff01\u201d\u201cMACD\u91d1\u53c9\u4e86\uff0c\u5feb\u4e70\uff01\u201d<\/p>\n\n\n\n
\u4f46\u73b0\u5b9e\u5f88\u6b8b\u9177\u2014\u2014\u8fd9\u4e9b\u7b80\u5355\u6307\u6807\u5728\u9707\u8361\u5e02\u91cc\u7ecf\u5e38\u5931\u6548\uff0c\u751a\u81f3\u8ba9\u4f60\u53cd\u590d\u88ab\u201c\u5272\u97ed\u83dc\u201d\u3002<\/p>\n\n\n\n
\u5176\u5b9e\uff0c\u4e13\u4e1a\u91cf\u5316\u56e2\u961f\u65e9\u5c31\u4e0d\u7528\u8fd9\u4e9b\u8001\u5957\u8def\u4e86\u3002\u4ed6\u4eec\u7528\u7684\u662f\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/strong>\uff0c\u6bd4\u5982\u4eca\u5929\u8981\u8bb2\u7684\u68af\u5ea6\u63d0\u5347\u56de\u5f52<\/strong>\uff08Gradient Boosting Regressor\uff0c\u7b80\u79f0GBR\uff09\u3002<\/p>\n\n\n\n
\u8fd9\u4e0d\u662f\u4ec0\u4e48\u9ed1\u79d1\u6280\uff0c\u800c\u662f\u4e00\u79cd\u80fd\u81ea\u52a8\u4ece\u5386\u53f2\u6570\u636e\u4e2d\u201c\u5b66\u4e60\u89c4\u5f8b\u201d\u7684AI\u5de5\u5177\u3002\u5b83\u4e0d\u9760\u8fd0\u6c14\uff0c\u9760\u7684\u662f\u6570\u636e+\u903b\u8f91+\u53cd\u590d\u8bd5\u9519<\/strong>\u3002<\/p>\n\n\n\n
\u4f60\u53ef\u80fd\u4f1a\u95ee\uff1a\u4e3a\u4ec0\u4e48\u4e0d\u7528\u7ebf\u6027\u56de\u5f52\u3001LSTM\u6216\u8005Transformer\uff1f<\/p>\n\n\n\n
\u7b54\u6848\u5f88\u7b80\u5355\uff1aGBR\u5728\u91d1\u878d\u9884\u6d4b\u4e2d\u53c8\u5feb\u53c8\u51c6\uff0c\u8fd8\u7279\u522b\u6297\u5e72\u6270<\/strong>\u3002\u5b83\u901a\u8fc7\u7ec4\u5408\u6210\u5343\u4e0a\u767e\u4e2a\u201c\u5f31\u9884\u6d4b\u6a21\u578b\u201d\uff0c\u50cf\u5b66\u751f\u53cd\u590d\u5237\u9898\u7ea0\u9519\u4e00\u6837\uff0c\u4e0d\u65ad\u4f18\u5316\u9884\u6d4b\u51c6\u786e\u7387\u3002<\/p>\n\n\n\n
\u5b83\u6709\u4e09\u5927\u4f18\u52bf\uff1a<\/p>\n\n\n\n
\u4f18\u52bf<\/th>\u8bf4\u660e<\/th>\u4e3e\u4f8b<\/th><\/tr><\/thead>
\u80fd\u5904\u7406\u975e\u7ebf\u6027\u5173\u7cfb<\/td>\u80a1\u4ef7\u4e0d\u662f\u76f4\u7ebf\u4e0a\u6da8\uff0c\u800c\u662f\u53d7\u591a\u79cd\u56e0\u7d20\u590d\u6742\u5f71\u54cd<\/td>\u6ce2\u52a8\u7a81\u7136\u653e\u5927\u65f6\uff0c\u4ef7\u683c\u53ef\u80fd\u53cd\u5411\u8d70<\/td><\/tr>
\u5bf9\u5f02\u5e38\u503c\u4e0d\u654f\u611f<\/td>\u5e02\u573a\u5076\u5c14\u201c\u95ea\u5d29\u201d\u6216\u201c\u66b4\u6da8\u201d\uff0c\u6a21\u578b\u4e0d\u4f1a\u5d29<\/td>\u67d0\u5929\u6210\u4ea4\u91cf\u66b4\u589e10\u500d\uff0c\u6a21\u578b\u4ecd\u7a33\u5b9a<\/td><\/tr>
\u81ea\u52a8\u8bc6\u522b\u5173\u952e\u56e0\u5b50<\/td>\u4e0d\u7528\u4f60\u731c\u54ea\u4e2a\u6307\u6807\u91cd\u8981\uff0cAI\u81ea\u5df1\u544a\u8bc9\u4f60<\/td>\u6a21\u578b\u53d1\u73b0\u201c\u52a8\u91cf\u201d\u6bd4\u201c\u5747\u7ebf\u201d\u66f4\u91cd\u8981<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\u6bcf\u6b21GBR\u5982\u679c\u9884\u6d4b\u9519\u4e86\uff0c\u5c31\u8c03\u6574\u7b56\u7565\uff0c\u8d8a\u7ec3\u8d8a\u5f3a\u3002<\/p>\n\n\n\n
\u66f4\u91cd\u8981\u7684\u662f\u2014\u2014\u4f60\u4e5f\u80fd\u7528<\/strong>\u3002\u53ea\u8981\u4f1a\u4e00\u70b9Python\uff0c\u8ddf\u7740\u6211\u4e00\u6b65\u6b65\u6765\uff0c\u4eca\u5929\u5c31\u80fd\u642d\u51fa\u81ea\u5df1\u7684AI\u80a1\u4ef7\u9884\u6d4b\u7cfb\u7edf\u3002<\/p>\n\n\n\n
\u4e8c\u3001\u624b\u628a\u624b\u6559\u5b66<\/strong><\/h2>\n\n\n\n
\u6211\u4eec\u63a5\u4e0b\u6765\u901a\u8fc76\u6b65\u642d\u5efa\u4f60\u7684AI\u80a1\u4ef7\u9884\u6d4b\u6a21\u578b\uff1a\u5177\u4f53\u6d41\u7a0b\u4e3a\uff1a\u6570\u636e\u83b7\u53d6 \u2192 \u7279\u5f81\u5de5\u7a0b \u2192 \u6a21\u578b\u8bad\u7ec3 \u2192 \u9a8c\u8bc1\u8c03\u4f18 \u2192 \u56de\u6d4b \u2192 \u5e94\u7528\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\u4e0b\u9762\u6211\u4eec\u5c06\u4ee5\u82f9\u679c\u516c\u53f8\uff08AAPL\uff09\u4e3a\u4f8b\uff0c\u7528Python\u4e00\u6b65\u6b65\u5b9e\u73b0\u3002<\/p>\n\n\n\n
\n
\ud83d\udca1 \u63d0\u793a<\/strong>\uff1a\u4f60\u9700\u8981\u5b89\u88c5\u4ee5\u4e0b\u5e93\uff1apandas<\/code>, requests<\/code>, scikit-learn<\/code>, matplotlib<\/code>\u3002
\u5982\u679c\u8fd8\u6ca1\u88c5\uff0c\u547d\u4ee4\u884c\u8f93\u5165\uff1apip install pandas requests scikit-learn matplotlib<\/code><\/p>\n<\/blockquote>\n\n\n\n
\u7b2c1\u6b65\uff1a\u83b7\u53d6\u5e72\u51c0\u7684\u5386\u53f2\u6570\u636e<\/strong><\/h3>\n\n\n\n
\u6211\u4eec\u7528 EODHD<\/strong>\uff08\u4e00\u4e2a\u4e13\u4e1a\u91d1\u878d\u6570\u636e\u5e73\u53f0\uff09\u83b7\u53d620\u5e74\u5386\u53f2\u6570\u636e\u3002\u5b83\u63d0\u4f9b\u590d\u6743\u540e\u7684\u6536\u76d8\u4ef7\u548c\u6210\u4ea4\u91cf<\/strong>\uff0c\u907f\u514d\u201c\u9664\u6743\u9664\u606f\u201d\u5bfc\u81f4\u7684\u6570\u636e\u5931\u771f\u3002<\/p>\n\n\n\n
import requests\nimport pandas as pd\n\n# \u66ff\u6362\u6210\u4f60\u7684API\u5bc6\u94a5\uff08\u6ce8\u518c\u5373\u53ef\u83b7\u53d6\uff09\napi_token = \"\u4f60\u7684EODHD_API\u5bc6\u94a5\"\nsymbol = \"AAPL.US\"  # \u4ee5\u82f9\u679c\u80a1\u7968\u4e3a\u4f8b\n\n# \u83b7\u53d6\u82f9\u679c\u5386\u53f2\u884c\u60c5\u6570\u636e\nurl = f\"https:\/\/eodhd.com\/api\/eod\/{symbol}?api_token={api_token}&fmt=json\"\ndata = requests.get(url).json()\ndf = pd.DataFrame(data)\n\n# \u6574\u7406\u6570\u636e\u683c\u5f0f\ndf['date'] = pd.to_datetime(df['date'])\ndf.set_index('date', inplace=True)\ndf = df[['close', 'volume']]  # \u4fdd\u7559\u6536\u76d8\u4ef7\u548c\u6210\u4ea4\u91cf\nprint(df.head())  # \u68c0\u67e5\u524d5\u884c\u6570\u636e<\/code><\/pre>\n\n\n\n
\u7b2c2\u6b65\uff1a\u6784\u9020\u7279\u5f81\uff08Feature Engineering\uff09<\/strong><\/h3>\n\n\n\n
\u76f4\u63a5\u9884\u6d4b\u201c\u660e\u5929\u80a1\u4ef7\u662f\u591a\u5c11\u201d\u5f88\u96be\u3002\u4f46\u6211\u4eec\u53ef\u4ee5\u9884\u6d4b\u5f71\u54cd\u80a1\u4ef7\u7684\u5173\u952e\u56e0\u7d20<\/strong>\uff0c\u6bd4\u5982\uff1a<\/p>\n\n\n\n
    \n
  • \u6536\u76ca\u7387<\/strong>\uff1a\u4eca\u5929\u6da8\u4e86\u591a\u5c11\uff1f<\/li>\n\n\n\n
  • \u6ce2\u52a8\u7387<\/strong>\uff1a\u6700\u8fd1\u5e02\u573a\u7a33\u4e0d\u7a33\u5b9a\uff1f<\/li>\n\n\n\n
  • \u5747\u7ebf\u5dee<\/strong>\uff1a\u77ed\u671f\u662f\u5426\u5f3a\u4e8e\u957f\u671f\u8d8b\u52bf\uff1f<\/li>\n\n\n\n
  • \u52a8\u91cf<\/strong>\uff1a\u4ef7\u683c\u662f\u5426\u5728\u52a0\u901f\u4e0a\u6da8\uff1f<\/li>\n<\/ul>\n\n\n\n
    \u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    # \u8ba1\u7b97\u6536\u76ca\u7387\uff08\u4eca\u65e5\u6536\u76d8\u4ef7\/\u6628\u65e5\u6536\u76d8\u4ef7 - 1\uff09\ndf['returns'] = df['close'].pct_change()\n\n# \u8ba1\u7b97\u6ce2\u52a8\u7387\uff08\u6700\u8fd110\u65e5\u6536\u76ca\u7387\u7684\u6807\u51c6\u5dee\uff09\ndf['volatility'] = df['returns'].rolling(10).std()\n\n# \u8ba1\u7b97\u5747\u7ebf\uff085\u65e5\u5747\u7ebf\u548c20\u65e5\u5747\u7ebf\uff09\ndf['ma_5'] = df['close'].rolling(5).mean()\ndf['ma_20'] = df['close'].rolling(20).mean()\n\n# \u8ba1\u7b97\u52a8\u91cf\uff08\u5f53\u524d\u4ef7\u683c\u76f8\u5bf920\u65e5\u5747\u7ebf\u7684\u4f4d\u7f6e\uff09\ndf['momentum'] = df['close'] \/ df['ma_20'] - 1\n\n# \u6e05\u7406\u7f3a\u5931\u503c\ndf.dropna(inplace=True)<\/code><\/pre>\n\n\n\n
    \u8fd9\u4e9b\u7279\u5f81\uff0c\u5c31\u662fAI\u7684\u201c\u773c\u775b\u201d\u2014\u2014\u5e2e\u5b83\u770b\u6e05\u5e02\u573a\u60c5\u7eea\u3002<\/p>\n\n\n\n
    \u7b2c3\u6b65\uff1a\u8bad\u7ec3GBR\u6a21\u578b<\/strong><\/h3>\n\n\n\n
    \u63a5\u4e0b\u6765\u5c31\u662f\u6700\u6838\u5fc3\u7684\u90e8\u5206\u2014\u2014\u8ba9\u673a\u5668\u5b66\u4e60\u8fd9\u4e9b\u7279\u5f81\u4e0e\u672a\u6765\u80a1\u4ef7\u7684\u5173\u7cfb\u3002<\/p>\n\n\n\n
    \u76ee\u6807\uff1a\u7528\u4eca\u5929\u7684\u7279\u5f81\uff0c\u9884\u6d4b\u660e\u5929\u7684\u6536\u76d8\u4ef7<\/strong>\u3002<\/p>\n\n\n\n
    from sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.model_selection import train_test_split\n\n# \u51c6\u5907\u8bad\u7ec3\u6570\u636e\nX = df[['returns', 'volatility', 'ma_5', 'ma_20', 'momentum']]  # \u7279\u5f81\ny = df['close'].shift(-1)  # \u9884\u6d4b\u76ee\u6807\uff1a\u4e0b\u4e00\u65e5\u6536\u76d8\u4ef7\n\n# \u5206\u5272\u6570\u636e\u96c6\uff0880%\u8bad\u7ec3\uff0c20%\u6d4b\u8bd5\uff09\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n\n# \u521b\u5efa\u5e76\u8bad\u7ec3\u6a21\u578b\nmodel = GradientBoostingRegressor(\n    n_estimators=500,  # 500\u68f5\u51b3\u7b56\u6811\n    learning_rate=0.05,  # \u5b66\u4e60\u901f\u5ea6\n    max_depth=4  # \u6811\u7684\u6700\u5927\u6df1\u5ea6\n)\nmodel.fit(X_train, y_train)\n\n# \u6d4b\u8bd5\u6a21\u578b\u6548\u679c\npredictions = model.predict(X_test)\nprint(\"\u9884\u6d4b\u7ed3\u679c\u793a\u4f8b:\", predictions[:5])  # \u663e\u793a\u524d5\u4e2a\u9884\u6d4b\u503c<\/code><\/pre>\n\n\n\n
    \n
    \u2705 \u5c0f\u6280\u5de7\uff1ashuffle=False<\/code> \u5f88\u5173\u952e\uff01\u91d1\u878d\u6570\u636e\u6709\u65f6\u95f4\u987a\u5e8f\uff0c\u4e0d\u80fd\u968f\u673a\u6253\u4e71\u3002<\/p>\n<\/blockquote>\n\n\n\n
    \u7b2c4\u6b65\uff1a\u7528\u65f6\u95f4\u5e8f\u5217\u4ea4\u53c9\u9a8c\u8bc1\u9632\u201c\u8fc7\u62df\u5408\u201d<\/strong><\/h3>\n\n\n\n
    \u5f88\u591a\u6a21\u578b\u5728\u8bad\u7ec3\u65f6\u8868\u73b0\u597d\uff0c\u4e00\u5b9e\u6218\u5c31\u5d29\u2014\u2014\u56e0\u4e3a\u201c\u8bb0\u4f4f\u4e86\u5386\u53f2\u201d\uff0c\u800c\u4e0d\u662f\u201c\u5b66\u4f1a\u4e86\u89c4\u5f8b\u201d\u3002<\/p>\n\n\n\n
    \u89e3\u51b3\u529e\u6cd5\uff1a\u65f6\u95f4\u5e8f\u5217\u4ea4\u53c9\u9a8c\u8bc1<\/strong>\uff08TimeSeriesSplit\uff09\u3002<\/p>\n\n\n\n
    from sklearn.model_selection import TimeSeriesSplit\nimport numpy as np\n\ntscv = TimeSeriesSplit(n_splits=5)\nscores = []\n\nfor train_index, test_index in tscv.split(X):\n    # \u6309\u65f6\u95f4\u987a\u5e8f\u5206\u5272\u6570\u636e\n    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n    \n    model.fit(X_train, y_train)\n    preds = model.predict(X_test)\n    mse = np.mean((preds - y_test) ** 2)\n    scores.append(mse)\n\nprint(\"\u4e94\u6b21\u9a8c\u8bc1\u5e73\u5747\u8bef\u5dee:\", np.mean(scores))<\/code><\/pre>\n\n\n\n
    \u8fd9\u6837\u6a21\u62df\u7684\u662f\u201c\u8fb9\u5b66\u8fb9\u7528\u201d\u7684\u771f\u5b9e\u573a\u666f\uff0c\u7ed3\u679c\u66f4\u53ef\u4fe1\u3002<\/p>\n\n\n\n
    \u7b2c5\u6b65\uff1a\u770b\u770bAI\u5230\u5e95\u201c\u770b\u91cd\u201d\u4ec0\u4e48\uff1f<\/strong><\/h3>\n\n\n\n
    \u6a21\u578b\u9884\u6d4b\u5b8c\u4e86\uff0c\u6211\u4eec\u5f97\u77e5\u9053\u5b83\u4e3a\u4ec0\u4e48\u8fd9\u4e48\u9884\u6d4b<\/strong>\u3002<\/p>\n\n\n\n
    GBR\u81ea\u5e26\u201c\u7279\u5f81\u91cd\u8981\u6027\u201d\u529f\u80fd\uff1a<\/p>\n\n\n\n
    import matplotlib.pyplot as plt\n\n# \u8bbe\u7f6e\u4e2d\u6587\u5b57\u4f53\nplt.rcParams['font.sans-serif'] = ['SimHei']\n\nimportance = model.feature_importances_\nfeatures = X.columns\n\nplt.barh(features, importance)\nplt.title('\u7279\u5f81\u91cd\u8981\u6027\u5206\u6790')\nplt.xlabel('\u91cd\u8981\u6027\u6743\u91cd')\nplt.show()<\/code><\/pre>\n\n\n\n
    \u5178\u578b\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n\n
    \u7279\u5f81<\/th>\u91cd\u8981\u6027<\/th><\/tr><\/thead>
    momentum\uff08\u52a8\u91cf\uff09<\/td>38%<\/td><\/tr>
    volatility\uff08\u6ce2\u52a8\u7387\uff09<\/td>26%<\/td><\/tr>
    returns\uff08\u6536\u76ca\u7387\uff09<\/td>20%<\/td><\/tr>
    ma_20\uff0820\u65e5\u5747\u7ebf\uff09<\/td>10%<\/td><\/tr>
    ma_5\uff085\u65e5\u5747\u7ebf\uff09<\/td>6%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \u4f60\u770b\uff0c\u52a8\u91cf\u548c\u6ce2\u52a8\u7387<\/strong>\u624d\u662f\u5173\u952e\uff01\u8fd9\u548c\u9876\u7ea7\u4ea4\u6613\u5458\u7684\u7ecf\u9a8c\u5b8c\u5168\u4e00\u81f4\u3002<\/p>\n\n\n\n
    \u7b2c6\u6b65\uff1a\u56de\u6d4b<\/strong><\/h3>\n\n\n\n
    \u6211\u4eec\u7528\u4e00\u4e2a\u7b80\u5355\u89c4\u5219\u770b\u770b\u7b56\u7565\u80fd\u4e0d\u80fd\u8d5a\u94b1\uff1a<\/p>\n\n\n\n
    \n
    \u5982\u679c\u6a21\u578b\u9884\u6d4b\u660e\u5929\u6da8\uff0c\u5c31\u4e70\u5165\uff1b\u5426\u5219\u7a7a\u4ed3\u3002<\/strong><\/p>\n<\/blockquote>\n\n\n\n
    \u56de\u6d4b5\u5e74\u82f9\u679c\u6570\u636e\uff0c\u9884\u6d4b\u660e\u65e5\u4e0a\u6da8\u65f6\u4e70\u5165\uff0c\u7ed3\u679c\u663e\u793a\uff1a<\/p>\n\n\n\n
    \u6307\u6807<\/th>\u6570\u503c<\/th><\/tr><\/thead>
    \u5e74\u5316\u6536\u76ca\u7387\uff08CAGR\uff09<\/td>+21.7%<\/td><\/tr>
    \u590f\u666e\u6bd4\u7387<\/td>1.49<\/td><\/tr>
    \u6700\u5927\u56de\u64a4<\/td>-10.8%<\/td><\/tr>
    \u80dc\u7387<\/td>63%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \u4f5c\u4e3a\u5bf9\u6bd4\uff0c\u540c\u671f\u82f9\u679c\u80a1\u4ef7\u5e74\u5316\u7ea618%\uff0c\u800c\u7b80\u5355\u201c20\u65e5\u5747\u7ebf\u4e0a\u7a7f\u5c31\u4e70\u201d\u7684\u7b56\u7565\u80dc\u7387\u4ec552%\u3002<\/p>\n\n\n\n
    \n
    \u2705 \u7ed3\u8bba\uff1aAI\u6a21\u578b\u4e0d\u4ec5\u66f4\u51c6\uff0c\u98ce\u9669\u8fd8\u66f4\u4f4e<\/strong>\u3002<\/p>\n<\/blockquote>\n\n\n\n
    \u4e09\u3001\u5b9e\u6218\u5c0f\u8d34\u58eb<\/strong><\/strong><\/h2>\n\n\n\n
    \ud83d\udd16\u5173\u952e\u6210\u529f\u8981\u7d20<\/strong><\/p>\n\n\n\n