diff --git a/doc/典型示例新版.md b/doc/典型示例新版.md
index b512d97..6725480 100644
--- a/doc/典型示例新版.md
+++ b/doc/典型示例新版.md
@@ -316,7 +316,8 @@ TEP数据集共包含53个特征列：
 1. **完整性检查**：确认数据集无缺失值
 2. **异常值处理**：使用IsolationForest方法识别并移除异常值
 3. **数据标准化**：采用StandardScaler方法将特征标准化到[-1, 1]区间
-4. **数据集划分**：按8:2比例划分训练集和测试集
+4. **特征提取**: 使用PCA将特征维度从53降低到20
+5. **数据集划分**：按8:2比例划分训练集和测试集
 
 **预处理配置**：
 ```json
@@ -327,7 +328,7 @@ TEP数据集共包含53个特征列：
         {
             "method_name": "IsolationForest",
             "params": {
-                "n_estimators": 100,
+                "n_estimators": 200,
                 "max_samples": "auto"
             }
         },
@@ -339,7 +340,14 @@ TEP数据集共包含53个特征列：
             }
         }
     ],
-    "feature_methods": [],
+    "feature_methods": [
+        {
+           "method_name":"PCA",
+           "params":{
+                "n_components": 20
+            }
+        }
+    ],
     "split_params": {
         "train_size": 0.8,
         "val_size": 0.2
diff --git a/doc/接口文档code.md b/doc/接口文档code.md
index 2b9a739..f2e319e 100644
--- a/doc/接口文档code.md
+++ b/doc/接口文档code.md
@@ -151,7 +151,14 @@ Request:
             }
         }
     ],
-    "feature_methods": [],
+    "feature_methods": [
+      {
+           "method_name":"PCA",
+           "params":{
+            "n_components": 20
+         }
+      }
+    ],
     "split_params": {
         "train_size": 0.8,
         "val_size": 0.2
diff --git a/function/__pycache__/data_manager.cpython-39.pyc b/function/__pycache__/data_manager.cpython-39.pyc
index c18e45b..c1e1baf 100644
Binary files a/function/__pycache__/data_manager.cpython-39.pyc and b/function/__pycache__/data_manager.cpython-39.pyc differ
diff --git a/function/data_manager.py b/function/data_manager.py
index 6be6719..daf5611 100644
--- a/function/data_manager.py
+++ b/function/data_manager.py
@@ -336,7 +336,10 @@ class DataManager:
         try:
             method_name = method['method_name']
             params = method.get('params', {})
-            columns = method.get('columns', df.drop('target', axis=1).columns)  # 排除target列
+            
+            df_columns = df.columns[:-1]
+            
+            columns = method.get('columns', df_columns)  # 排除target列
             
             if method_name not in self.feature_engineering_methods:
                 raise ValueError(f"Unknown feature engineering method: {method_name}")
@@ -445,7 +448,7 @@ class DataManager:
                 train_data, val_data = train_test_split(
                     train_val_data,
                     test_size=val_size_adjusted,
-                    random_state=42
+                    random_state=random_state
                 )
             else:
                 train_data = train_val_data