diff --git a/function/data_processor_date.py b/function/data_processor_date.py
index 6e6b069..d264cf3 100644
--- a/function/data_processor_date.py
+++ b/function/data_processor_date.py
@@ -211,19 +211,27 @@ class DataProcessor:
                 
             processor = self.preprocessing_methods[method_name](**params)
             
+            # 分离特征和标签
+            features = df.drop('target', axis=1)
+            target = df['target']
+            
             # 根据不同类型的方法进行处理
             if method_name in ['IsolationForest', 'OneClassSVM', 'LocalOutlierFactor', 'EllipticEnvelope']:
                 # 异常值检测方法
-                mask = processor.fit_predict(df) != -1
-                df = df[mask]
+                mask = processor.fit_predict(features) != -1
+                features = features[mask]
+                target = target[mask]
             else:
                 # 其他预处理方法
-                df = pd.DataFrame(
-                    processor.fit_transform(df),
-                    columns=df.columns,
-                    index=df.index
+                features = pd.DataFrame(
+                    processor.fit_transform(features),
+                    columns=features.columns,
+                    index=features.index
                 )
             
+            # 重新组合特征和标签
+            df = pd.concat([features, target], axis=1)
+            
             self.logger.info(f"Applied preprocessing method {method_name}")
             return df
             
@@ -236,17 +244,21 @@ class DataProcessor:
         try:
             method_name = method['method_name']
             params = method.get('params', {})
-            columns = method.get('columns', df.columns)
+            columns = method.get('columns', df.drop('target', axis=1).columns)  # 排除target列
             
             if method_name not in self.feature_engineering_methods:
                 raise ValueError(f"Unknown feature engineering method: {method_name}")
                 
             processor = self.feature_engineering_methods[method_name](**params)
             
+            # 分离特征和标签
+            features = df.drop('target', axis=1)
+            target = df['target']
+            
             # 根据不同类型的特征工程方法进行处理
             if method_name in ['LabelEncoder', 'OneHotEncoder']:
                 # 编码方法
-                df_temp = df[columns].copy()
+                df_temp = features[columns].copy()
                 if method_name == 'LabelEncoder':
                     for col in columns:
                         df_temp[col] = processor.fit_transform(df_temp[col])
@@ -256,17 +268,17 @@ class DataProcessor:
                         encoded = pd.DataFrame(
                             encoded,
                             columns=[f"{col}_{i}" for col in columns for i in range(encoded.shape[1]//len(columns))],
-                            index=df.index
+                            index=features.index
                         )
                     df_temp = encoded
                 
-                # 更新原始数据框
-                df = df.drop(columns=columns)
-                df = pd.concat([df, df_temp], axis=1)
+                # 更新特征数据框
+                features = features.drop(columns=columns)
+                features = pd.concat([features, df_temp], axis=1)
                 
             elif method_name in ['KBinsDiscretizer']:
                 # 离散化方法
-                transformed = processor.fit_transform(df[columns])
+                transformed = processor.fit_transform(features[columns])
                 if processor.encode == 'onehot':
                     feature_names = [f"{col}_{i}" for col in columns for i in range(processor.n_bins_)]
                 else:
@@ -275,33 +287,36 @@ class DataProcessor:
                 df_temp = pd.DataFrame(
                     transformed,
                     columns=feature_names,
-                    index=df.index
+                    index=features.index
                 )
-                df = df.drop(columns=columns)
-                df = pd.concat([df, df_temp], axis=1)
+                features = features.drop(columns=columns)
+                features = pd.concat([features, df_temp], axis=1)
                 
             elif method_name in ['PCA', 'SelectKBest', 'RFE']:
                 # 降维和特征选择方法
-                transformed = processor.fit_transform(df[columns])
+                transformed = processor.fit_transform(features[columns])
                 n_features = transformed.shape[1]
                 df_temp = pd.DataFrame(
                     transformed,
                     columns=[f"feature_{i}" for i in range(n_features)],
-                    index=df.index
+                    index=features.index
                 )
-                df = df.drop(columns=columns)
-                df = pd.concat([df, df_temp], axis=1)
+                features = features.drop(columns=columns)
+                features = pd.concat([features, df_temp], axis=1)
                 
             else:
                 # 其他特征工程方法
-                transformed = processor.fit_transform(df[columns])
+                transformed = processor.fit_transform(features[columns])
                 df_temp = pd.DataFrame(
                     transformed,
                     columns=[f"{col}_transformed" for col in columns],
-                    index=df.index
+                    index=features.index
                 )
-                df = df.drop(columns=columns)
-                df = pd.concat([df, df_temp], axis=1)
+                features = features.drop(columns=columns)
+                features = pd.concat([features, df_temp], axis=1)
+            
+            # 重新组合特征和标签
+            df = pd.concat([features, target], axis=1)
             
             self.logger.info(f"Applied feature engineering method {method_name}")
             return df