diff --git a/src/core/steps/modeling_step.py b/src/core/steps/modeling_step.py
index 405e4bc..b58d20f 100644
--- a/src/core/steps/modeling_step.py
+++ b/src/core/steps/modeling_step.py
@@ -14,6 +14,114 @@ import pandas as pd
 import numpy as np
 
 
+# ============================================================
+# 汉化 -> 英文 反向映射字典（UI 复选框显示文本 -> 底层算法键名）
+# ============================================================
+
+# 模型名称：中文 (缩写) -> 英文键名
+MODEL_NAME_MAP = {
+    "多元线性回归 (MLR)": "LinearRegression",
+    "岭回归 (Ridge)": "Ridge",
+    "套索回归 (Lasso)": "Lasso",
+    "弹性网络 (ElasticNet)": "ElasticNet",
+    "偏最小二乘 (PLSR)": "PLS",
+    "决策树 (CART)": "DecisionTree",
+    "随机森林 (RF)": "RF",
+    "极端随机树 (ET)": "ExtraTrees",
+    "极值梯度提升 (XGBoost)": "XGBoost",
+    "轻量梯度提升 (LightGBM)": "LightGBM",
+    "类别梯度提升 (CatBoost)": "CatBoost",
+    "梯度提升树 (GBDT)": "GradientBoosting",
+    "自适应提升 (AdaBoost)": "AdaBoost",
+    "支持向量回归 (SVR)": "SVR",
+    "K近邻回归 (KNN)": "KNN",
+    "多层感知机 (BP神经网络)": "MLP",
+}
+
+# 预处理方法：各种可能的中文变体 -> 标准键名
+PREPROC_NAME_MAP = {
+    # 无处理
+    "无 (None)": "None",
+    "None": "None",
+    # MMS
+    "最小-最大归一化 (MMS)": "MMS",
+    "MMS": "MMS",
+    # SS
+    "标度化 (SS)": "SS",
+    "SS": "SS",
+    # SNV
+    "标准正态变换 (SNV)": "SNV",
+    "SNV": "SNV",
+    # MA
+    "移动平均 (MA)": "MA",
+    "MA": "MA",
+    # SG
+    "Savitzky-Golay (SG)": "SG",
+    "SG": "SG",
+    # MSC
+    "多元散射校正 (MSC)": "MSC",
+    "MSC": "MSC",
+    # D1
+    "一阶导数 (D1)": "D1",
+    "D1": "D1",
+    # D2
+    "二阶导数 (D2)": "D2",
+    "D2": "D2",
+    # DT
+    "去趋势 (DT)": "DT",
+    "DT": "DT",
+    # CT
+    "中心化 (CT)": "CT",
+    "CT": "CT",
+}
+
+# 数据划分方法：各种可能的中文变体 -> 标准键名
+SPLIT_NAME_MAP = {
+    "SPXY 算法 (考量X-Y空间)": "spxy",
+    "spxy": "spxy",
+    "KS 算法 (考量X空间)": "ks",
+    "ks": "ks",
+    "随机划分 (Random)": "random",
+    "random": "random",
+}
+
+
+def _normalize_model_names(model_names: List[str]) -> List[str]:
+    """清洗模型名称列表：将汉化显示文本还原为英文键名"""
+    result = []
+    for name in model_names:
+        if name in MODEL_NAME_MAP:
+            result.append(MODEL_NAME_MAP[name])
+        else:
+            # 已经是英文键名，直接保留
+            result.append(name)
+    return result
+
+
+def _normalize_preprocessing_methods(methods: List[str]) -> List[str]:
+    """清洗预处理方法列表：将汉化显示文本还原为标准键名"""
+    result = []
+    for method in methods:
+        if method in PREPROC_NAME_MAP:
+            result.append(PREPROC_NAME_MAP[method])
+        else:
+            # 已经是标准键名，直接保留
+            result.append(method)
+    return result
+
+
+def _normalize_split_methods(methods: List[str]) -> List[str]:
+    """清洗数据划分方法列表：将汉化显示文本还原为标准键名"""
+    result = []
+    for method in methods:
+        if method in SPLIT_NAME_MAP:
+            result.append(SPLIT_NAME_MAP[method])
+        else:
+            # 已经是标准键名，直接保留
+            result.append(method)
+    return result
+
+
 class ModelingStep:
     """建模步骤"""
 
@@ -75,6 +183,15 @@ class ModelingStep:
         if split_methods is None:
             split_methods = ["spxy", "ks", "random"]
 
+        # ---- 汉化清洗：将 UI 传来的中文/混合名称转换为底层英文键名 ----
+        preprocessing_methods = _normalize_preprocessing_methods(preprocessing_methods)
+        model_names = _normalize_model_names(model_names)
+        split_methods = _normalize_split_methods(split_methods)
+
+        print(f"[参数清洗] 预处理方法: {preprocessing_methods}")
+        print(f"[参数清洗] 模型名称: {model_names}")
+        print(f"[参数清洗] 划分方法: {split_methods}")
+
         modeler = WaterQualityModelingBatch(str(output_dir))
         modeler.train_models_batch(
             csv_path=training_csv_path,
diff --git a/src/gui/panels/step6_panel.py b/src/gui/panels/step6_panel.py
index 81e1e6b..729697e 100644
--- a/src/gui/panels/step6_panel.py
+++ b/src/gui/panels/step6_panel.py
@@ -17,6 +17,57 @@ from src.gui.components.custom_widgets import FileSelectWidget
 from src.gui.styles import ModernStylesheet
 
 
+# ============================================================
+# 中文映射表（内部键名 -> 显示文本）
+# ============================================================
+
+# 预处理方法：内部键 -> 显示文本
+PREPROC_CHINESE = {
+    'None': '无 (None)',
+    'MMS': '最小-最大归一化 (MMS)',
+    'SS': '标度化 (SS)',
+    'SNV': '标准正态变换 (SNV)',
+    'MA': '移动平均 (MA)',
+    'SG': 'Savitzky-Golay (SG)',
+    'MSC': '多元散射校正 (MSC)',
+    'D1': '一阶导数 (D1)',
+    'D2': '二阶导数 (D2)',
+    'DT': '去趋势 (DT)',
+    'CT': '中心化 (CT)',
+}
+
+# 模型类型：内部键 -> 显示文本
+MODEL_CHINESE = {
+    # 线性模型
+    'LinearRegression': '多元线性回归 (MLR)',
+    'Ridge': '岭回归 (Ridge)',
+    'Lasso': '套索回归 (Lasso)',
+    'ElasticNet': '弹性网络 (ElasticNet)',
+    'PLS': '偏最小二乘 (PLSR)',
+    # 树模型
+    'DecisionTree': '决策树 (CART)',
+    'RF': '随机森林 (RF)',
+    'ExtraTrees': '极端随机树 (ET)',
+    'XGBoost': '极值梯度提升 (XGBoost)',
+    'LightGBM': '轻量梯度提升 (LightGBM)',
+    'CatBoost': '类别梯度提升 (CatBoost)',
+    # 集成学习
+    'GradientBoosting': '梯度提升树 (GBDT)',
+    'AdaBoost': '自适应提升 (AdaBoost)',
+    # 其他模型
+    'SVR': '支持向量回归 (SVR)',
+    'KNN': 'K近邻回归 (KNN)',
+    'MLP': '多层感知机 (BP神经网络)',
+}
+
+# 数据划分方法：内部键 -> 显示文本
+SPLIT_CHINESE = {
+    'spxy': 'SPXY 算法 (考量X-Y空间)',
+    'ks': 'KS 算法 (考量X空间)',
+    'random': '随机划分 (Random)',
+}
+
+
 class Step6Panel(QWidget):
     """步骤6：机器学习建模"""
     def __init__(self, parent=None):
@@ -54,7 +105,7 @@ class Step6Panel(QWidget):
 
         # 启用步骤
         self.enable_checkbox = QCheckBox("启用此步骤")
-        self.enable_checkbox.setChecked(True)
+        self.enable_checkbox.setChecked(False)
         layout.addWidget(self.enable_checkbox)
 
         # 独立运行按钮
@@ -95,8 +146,8 @@ class Step6Panel(QWidget):
         preproc_methods = ['None', 'MMS', 'SS', 'SNV', 'MA', 'SG', 'MSC', 'D1', 'D2', 'DT', 'CT']
 
         for i, method in enumerate(preproc_methods):
-            checkbox = QCheckBox(method)
-            checkbox.setChecked(True)
+            checkbox = QCheckBox(PREPROC_CHINESE.get(method, method))
+            checkbox.setChecked(False)
             self.preproc_checkboxes[method] = checkbox
             preproc_grid.addWidget(checkbox, i // 4, i % 4)
 
@@ -122,10 +173,10 @@ class Step6Panel(QWidget):
         self.model_checkboxes = {}
 
         model_groups = [
-            ("线性模型", ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'PLS']),
-            ("树模型", ['DecisionTree', 'RF', 'ExtraTrees', 'XGBoost', 'LightGBM', 'CatBoost']),
-            ("集成学习", ['GradientBoosting', 'AdaBoost']),
-            ("其他模型", ['SVR', 'KNN', 'MLP'])
+            ("【线性模型】", ['LinearRegression', 'Ridge', 'Lasso', 'ElasticNet', 'PLS']),
+            ("【树模型】", ['DecisionTree', 'RF', 'ExtraTrees', 'XGBoost', 'LightGBM', 'CatBoost']),
+            ("【集成学习】", ['GradientBoosting', 'AdaBoost']),
+            ("【其他模型】", ['SVR', 'KNN', 'MLP'])
         ]
 
         row = 0
@@ -140,8 +191,8 @@ class Step6Panel(QWidget):
             row += 1
 
             for i, model in enumerate(models):
-                checkbox = QCheckBox(model)
-                checkbox.setChecked(model in ['SVR', 'RF', 'Ridge', 'Lasso'])
+                checkbox = QCheckBox(MODEL_CHINESE.get(model, model))
+                checkbox.setChecked(False)
                 self.model_checkboxes[model] = checkbox
                 model_grid.addWidget(checkbox, row, i % 4)
                 if (i + 1) % 4 == 0:
@@ -172,8 +223,8 @@ class Step6Panel(QWidget):
         split_methods = ['spxy', 'ks', 'random']
 
         for i, method in enumerate(split_methods):
-            checkbox = QCheckBox(method)
-            checkbox.setChecked(True)
+            checkbox = QCheckBox(SPLIT_CHINESE.get(method, method))
+            checkbox.setChecked(False)
             self.split_checkboxes[method] = checkbox
             split_grid.addWidget(checkbox, 0, i)