diff --git a/src/gui/panels/step9_ml_predict_panel.py b/src/gui/panels/step9_ml_predict_panel.py
index dfe7217..f69e93f 100644
--- a/src/gui/panels/step9_ml_predict_panel.py
+++ b/src/gui/panels/step9_ml_predict_panel.py
@@ -233,6 +233,14 @@ class Step9MlPredictPanel(QWidget):
             return
 
         self.external_model_dir = dir_path
+
+        # ★ 读取用户选择的模型评估指标
+        _metric_key = self.metric.currentData()      # "test_r2" / "test_rmse" / "test_mae"
+        _metric_display = self.metric.currentText()   # "R² (决定系数)" / ...
+        _is_r2 = 'r2' in _metric_key.lower()
+        _is_rmse = 'rmse' in _metric_key.lower()
+        _is_mae = 'mae' in _metric_key.lower()
+
         models_found = {}
         errors = []
 
@@ -243,26 +251,53 @@ class Step9MlPredictPanel(QWidget):
                 if not subentry.is_dir():
                     continue
                 subdir_name = subentry.name
-                joblib_files = [
+                joblib_files = sorted([
                     f for f in os.scandir(subentry.path)
                     if f.is_file() and f.name.lower().endswith(".joblib")
-                ]
+                ], key=lambda x: x.name)
                 if not joblib_files:
                     continue
-                joblib_path = joblib_files[0].path
-                try:
-                    loaded = joblib.load(joblib_path)
-                    if isinstance(loaded, dict) and "model" in loaded:
-                        # ★ 保留完整 dict（含 metadata / train_wavelengths），
-                        #   推理端需要 train_wavelengths 做光谱重采样
-                        models_found[subdir_name] = loaded
-                    elif hasattr(loaded, "predict"):
-                        models_found[subdir_name] = loaded
-                    else:
-                        errors.append(f"{subdir_name}: 无法识别的格式 {type(loaded).__name__}")
+
+                # ★ 遍历该子目录下全部 .joblib 文件，按 metric 选出最佳模型
+                best_score = -float('inf') if _is_r2 else float('inf')
+                best_entry = None
+                best_fname = None
+
+                for f_entry in joblib_files:
+                    try:
+                        data = joblib.load(f_entry.path)
+                        if not isinstance(data, dict) or "model" not in data:
+                            continue
+                        meta = data.get('metadata', {})
+                        # 按指标优先级：test_xxx → train_xxx
+                        score = None
+                        if _is_r2:
+                            score = meta.get('test_r2', meta.get('train_r2', None))
+                        elif _is_rmse:
+                            score = meta.get('test_rmse', meta.get('train_rmse', None))
+                        elif _is_mae:
+                            score = meta.get('test_mae', meta.get('train_mae', None))
+
+                        if score is None:
+                            continue
+
+                        if _is_r2 and score > best_score:
+                            best_score = score
+                            best_entry = data
+                            best_fname = f_entry.name
+                        elif not _is_r2 and score < best_score:
+                            best_score = score
+                            best_entry = data
+                            best_fname = f_entry.name
+                    except Exception:
                         continue
-                except Exception as e:
-                    errors.append(f"{subdir_name}: {type(e).__name__}: {e}")
+
+                if best_entry is not None:
+                    models_found[subdir_name] = best_entry
+                    print(f"[模型加载] 目标 {subdir_name} 根据 {_metric_display} "
+                          f"自动选择最佳模型: {best_fname}, 分数: {best_score:.6f}")
+                else:
+                    errors.append(f"{subdir_name}: 无可评估的 .joblib 文件")
 
         except Exception as e:
             QMessageBox.warning(