diff --git a/src/core/prediction/inference_batch.py b/src/core/prediction/inference_batch.py
index b392bc0..3536187 100644
--- a/src/core/prediction/inference_batch.py
+++ b/src/core/prediction/inference_batch.py
@@ -662,6 +662,28 @@ class WaterQualityInference:
                     print(f"[兼容填充] 特征不足，补零 "
                           f"{n_current} → {expected_features} 列")
 
+        # ═══════════════════════════════════════════════════════════
+        # ★ 特征补全：模型训练时可能包含 WQI 指数等衍生特征，
+        #   推理端需自动计算补齐（适用于新旧模型两条路径）
+        # ═══════════════════════════════════════════════════════════
+        expected_features = getattr(model, 'n_features_in_', None)
+        if expected_features is not None and spectra.shape[1] < expected_features:
+            print(f"[特征补全] 检测到特征缺口：当前 {spectra.shape[1]} 列 "
+                  f"< 模型期望 {expected_features} 列，正在计算 WQI 指数...")
+            try:
+                from src.utils.water_index import WaterQualityIndexCalculator
+                calc = WaterQualityIndexCalculator()
+                formulas = calc.list_available()
+                if formulas:
+                    results_df = calc.calculate_many(formulas, spectra, fast=True)
+                    if isinstance(results_df, pd.DataFrame) and not results_df.empty:
+                        original_col_count = spectra.shape[1]
+                        spectra = pd.concat([spectra, results_df], axis=1)
+                        print(f"[特征补全] 完成！扩充至 {spectra.shape[1]} 列 "
+                              f"(+{spectra.shape[1] - original_col_count} WQI)")
+            except Exception as e:
+                print(f"[特征补全] 失败: {e}")
+
         # ═══════════════════════════════════════════════════════════
         # 通用清洗
         # ═══════════════════════════════════════════════════════════