diff --git a/Evaluate.py b/Evaluate.py
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+++ b/Evaluate.py
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+"""
+evaluate_and_plot.py
+加载模型，在验证集上推理，绘制混淆矩阵 / ROC / PR 曲线
+"""
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+import torch
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from sklearn.metrics import (
+    confusion_matrix, ConfusionMatrixDisplay,
+    roc_curve, auc,
+    precision_recall_curve, average_precision_score,
+)
+
+from Model import Net
+from Dataloader import RobustImageFolder
+
+matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
+matplotlib.rcParams['axes.unicode_minus'] = False
+
+# ============================================================
+# ★★★ 需要你修改的参数 ★★★
+# ============================================================
+MODEL_PATH  = 'best_model.pth'                                    # 模型权重路径
+DATA_ROOT   = '../trash_division_data/ultimate_4_class/'          # 数据集根目录
+BATCH_SIZE  = 64
+IMAGE_SIZE  = 256
+NUM_WORKERS = 4
+# ============================================================
+
+# ---------- 1. 加载验证集 ----------
+val_transform = transforms.Compose([
+    transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225]),
+])
+
+val_dataset = RobustImageFolder(
+    root=os.path.join(DATA_ROOT, 'val'),
+    transform=val_transform,
+)
+val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE,
+                        shuffle=False, num_workers=NUM_WORKERS,
+                        pin_memory=True, drop_last=False)
+
+class_names = val_dataset.classes
+num_classes = len(class_names)
+print(f"类别: {class_names}")
+
+# ---------- 2. 加载模型 ----------
+device = torch.device('xpu' if torch.xpu.is_available() else 'cpu')
+print(device)
+model = Net(num_classes=num_classes)
+state_dict = torch.load(MODEL_PATH, map_location=device)
+if 'model_state_dict' in state_dict:
+    state_dict = state_dict['model_state_dict']
+elif 'model' in state_dict:
+    state_dict = state_dict['model']
+model.load_state_dict(state_dict)
+model = model.to(device).eval()
+print("模型加载完成")
+
+# ---------- 3. 推理 ----------
+all_labels = []
+all_probs  = []
+
+with torch.no_grad():
+    for images, labels in val_loader:
+        images = images.to(device)
+        probs = torch.softmax(model(images), dim=1)
+        all_labels.append(labels.numpy())
+        all_probs.append(probs.cpu().numpy())
+
+all_labels = np.concatenate(all_labels)
+all_probs  = np.concatenate(all_probs)
+all_preds  = np.argmax(all_probs, axis=1)
+print(f"推理完成, 共 {len(all_labels)} 样本")
+
+# ============================================================
+# ① 混淆矩阵
+# ============================================================
+cm = confusion_matrix(all_labels, all_preds)
+fig, ax = plt.subplots(figsize=(8, 7))
+ConfusionMatrixDisplay(cm, display_labels=class_names).plot(
+    ax=ax, cmap='Blues', values_format='d', xticks_rotation=30)
+ax.set_title('Confusion Matrix', fontsize=14)
+plt.tight_layout()
+plt.savefig('confusion_matrix.png', dpi=150, bbox_inches='tight')
+plt.show()
+print("混淆矩阵已保存: confusion_matrix.png")
+
+# ============================================================
+# ② ROC 曲线 (One-vs-Rest + Macro-average)
+# ============================================================
+one_hot = np.eye(num_classes)[all_labels]
+colors  = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+
+fig, ax = plt.subplots(figsize=(8, 7))
+fpr_d, tpr_d, auc_d = {}, {}, {}
+
+for i in range(num_classes):
+    fpr_d[i], tpr_d[i], _ = roc_curve(one_hot[:, i], all_probs[:, i])
+    auc_d[i] = auc(fpr_d[i], tpr_d[i])
+    ax.plot(fpr_d[i], tpr_d[i], color=colors[i], lw=2,
+            label=f'{class_names[i]} (AUC={auc_d[i]:.4f})')
+
+# Macro-average
+all_fpr  = np.unique(np.concatenate([fpr_d[i] for i in range(num_classes)]))
+mean_tpr = sum(np.interp(all_fpr, fpr_d[i], tpr_d[i]) for i in range(num_classes)) / num_classes
+macro_auc = auc(all_fpr, mean_tpr)
+ax.plot(all_fpr, mean_tpr, 'navy', lw=2, ls='--',
+        label=f'Macro-avg (AUC={macro_auc:.4f})')
+ax.plot([0, 1], [0, 1], 'k--', lw=1, alpha=0.5)
+
+ax.set_xlim(0, 1); ax.set_ylim(0, 1.05)
+ax.set_xlabel('False Positive Rate'); ax.set_ylabel('True Positive Rate')
+ax.set_title('ROC Curve', fontsize=14)
+ax.legend(loc='lower right'); ax.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig('roc_curve.png', dpi=150, bbox_inches='tight')
+plt.show()
+print("ROC 曲线已保存: roc_curve.png")
+
+# ============================================================
+# ③ Precision-Recall 曲线
+# ============================================================
+fig, ax = plt.subplots(figsize=(8, 7))
+
+for i in range(num_classes):
+    prec, rec, _ = precision_recall_curve(one_hot[:, i], all_probs[:, i])
+    ap = average_precision_score(one_hot[:, i], all_probs[:, i])
+    ax.plot(rec, prec, color=colors[i], lw=2,
+            label=f'{class_names[i]} (AP={ap:.4f})')
+
+ax.set_xlim(0, 1); ax.set_ylim(0, 1.05)
+ax.set_xlabel('Recall'); ax.set_ylabel('Precision')
+ax.set_title('Precision-Recall Curve', fontsize=14)
+ax.legend(loc='best'); ax.grid(True, alpha=0.3)
+plt.tight_layout()
+plt.savefig('pr_curve.png', dpi=150, bbox_inches='tight')
+plt.show()
+print("PR 曲线已保存: pr_curve.png")