diff --git a/frontend/dist/index.html b/frontend/dist/index.html
index cf342c2..6566968 100644
--- a/frontend/dist/index.html
+++ b/frontend/dist/index.html
@@ -6,8 +6,8 @@
     <meta name="viewport" content="width=device-width,initial-scale=1.0">
     <link rel="icon" href="/favicon.ico">
     <title>装备成本估算系统</title>
-    <script type="module" crossorigin src="/assets/index-9zP5mdZu.js"></script>
-    <link rel="stylesheet" crossorigin href="/assets/index-xz9THzb6.css">
+    <script type="module" crossorigin src="/assets/index-BuNHhthn.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-BEQL0wGd.css">
   </head>
   <body>
     <noscript>
diff --git a/frontend/src/views/PredictPage.vue b/frontend/src/views/PredictPage.vue
index 5bfecb2..dc9b932 100644
--- a/frontend/src/views/PredictPage.vue
+++ b/frontend/src/views/PredictPage.vue
@@ -14,6 +14,22 @@
           </el-select>
         </el-form-item>
         
+        <!-- 预测模型选择 -->
+        <el-form-item label="预测模型">
+          <el-select v-model="selectedModelId" placeholder="自动选择最优" style="width: 100%" @change="handleModelChange">
+            <el-option label="自动选择（最优模型）" :value="null" />
+            <el-option
+              v-for="m in availableModels"
+              :key="m.id"
+              :label="`${getModelLabel(m)}`"
+              :value="m.id"
+            >
+              <span>{{ getModelLabel(m) }}</span>
+              <span style="float: right; color: #8492a6; font-size: 13px">R²={{ m.r2 }}</span>
+            </el-option>
+          </el-select>
+        </el-form-item>
+        
         <!-- 通用参数 -->
         <el-form-item label="总长(m)" prop="length_m">
           <el-input-number v-model="formData.length_m" :precision="2"></el-input-number>
@@ -238,6 +254,8 @@ const formData = reactive({
 const predictionResults = ref(null)
 const mlPrediction = ref(null)
 const plsPrediction = ref(null)
+const availableModels = ref([])
+const selectedModelId = ref(null)
 
 const submitForm = async () => {
   try {
@@ -277,10 +295,16 @@ const submitForm = async () => {
       }
     }
     
+    // 构建预测请求数据
+    const predictData = { ...formData }
+    if (selectedModelId.value) {
+      predictData.model_id = selectedModelId.value
+    }
+    
     // 同时调用两个预测接口
     const [mlResponse, plsResponse] = await Promise.all([
-      axios.post(`${API_BASE_URL}/predict`, formData),
-      axios.post(`${API_BASE_URL}/pls/predict`, formData)
+      axios.post(`${API_BASE_URL}/predict`, predictData),
+      axios.post(`${API_BASE_URL}/pls/predict`, predictData)
     ])
     
     mlPrediction.value = mlResponse.data
@@ -352,6 +376,13 @@ const handleTypeChange = () => {
   predictionResults.value = false
   mlPrediction.value = null
   plsPrediction.value = null
+  selectedModelId.value = null
+  availableModels.value = []
+  
+  // 加载可用模型
+  if (formData.type) {
+    loadModels(formData.type)
+  }
   
   // 重置特有参数
   if (formData.type === '火箭炮') {
@@ -425,6 +456,30 @@ const handleTypeChange = () => {
     formData.travel_range_km = null
   }
 }
+
+const loadModels = async (equipmentType) => {
+  try {
+    const res = await axios.get(`${API_BASE_URL}/models/predict`, {
+      params: { equipment_type: equipmentType }
+    })
+    availableModels.value = res.data
+  } catch (e) {
+    console.error('Failed to load models:', e)
+  }
+}
+
+const handleModelChange = () => {
+  predictionResults.value = false
+  mlPrediction.value = null
+}
+
+const getModelLabel = (m) => {
+  const names = {
+    'pytorch': 'PyTorch', 'xgboost': 'XGBoost', 'lightgbm': 'LightGBM',
+    'gbm': 'GBM', 'rf': 'Random Forest', 'pls': 'PLS回归'
+  }
+  return `${names[m.type] || m.type}  R²=${m.r2}  MAE=${Number(m.mae).toLocaleString()}`
+}
 </script>
 
 <style lang="scss" scoped>
diff --git a/src/routes.py b/src/routes.py
index b7b2de0..daa4fe4 100644
--- a/src/routes.py
+++ b/src/routes.py
@@ -104,19 +104,27 @@ def predict():
     try:
         data = request.get_json()
         equipment_type = data.get('type')
+        model_id = data.get('model_id')  # 可选：指定模型ID
         logger.info(f"Received prediction request for equipment type: {equipment_type}")
         
-        # 获取最新的激活模型（非PLS），尝试加载直到成功
+        # 获取可用模型（默认R²最高的；可选指定model_id）
         with get_db_connection() as conn:
             cursor = conn.cursor()
-            cursor.execute("""
-                SELECT * FROM trained_models 
-                WHERE equipment_type = ? 
-                AND model_type != 'pls'
-                AND is_active = TRUE
-                ORDER BY training_date DESC
-            """, (equipment_type,))
-            models = cursor.fetchall()
+            if model_id:
+                cursor.execute("""
+                    SELECT * FROM trained_models 
+                    WHERE id = ? AND is_active = TRUE
+                """, (model_id,))
+                models = cursor.fetchall()
+            else:
+                cursor.execute("""
+                    SELECT * FROM trained_models 
+                    WHERE equipment_type = ? 
+                    AND model_type != 'pls'
+                    AND is_active = TRUE
+                    ORDER BY r2_score DESC
+                """, (equipment_type,))
+                models = cursor.fetchall()
         
         if not models:
             return jsonify({'error': '未找到可用的模型'}), 404
@@ -148,6 +156,36 @@ def predict():
         logger.error("Detailed traceback:", exc_info=True)
         return jsonify({'error': str(e)}), 500
 
+@api_bp.route('/models/predict', methods=['GET'])
+def get_predict_models():
+    """获取可用于预测的模型列表（含精度指标，按R²排序）"""
+    try:
+        equipment_type = request.args.get('equipment_type', '')
+        if not equipment_type:
+            return jsonify({'error': '请提供装备类型'}), 400
+            
+        with get_db_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                SELECT id, model_name, model_type, r2_score, mae, rmse
+                FROM trained_models 
+                WHERE equipment_type = ? AND is_active = TRUE
+                ORDER BY r2_score DESC
+            """, (equipment_type,))
+            models = cursor.fetchall()
+        
+        return jsonify([{
+            'id': m['id'],
+            'name': m['model_name'],
+            'type': m['model_type'],
+            'r2': round(m['r2_score'], 4) if m['r2_score'] else 0,
+            'mae': round(m['mae'], 2) if m['mae'] else 0,
+            'rmse': round(m['rmse'], 2) if m['rmse'] else 0,
+        } for m in models])
+    except Exception as e:
+        logger.error(f"Error fetching predict models: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
 @api_bp.route('/analyze-features', methods=['POST'])
 def analyze_features():
     """基于数据集进行特征分析"""
