diff --git a/train/food_classifier.py b/train/food_classifier.py
index 371a181..d43f51d 100644
--- a/train/food_classifier.py
+++ b/train/food_classifier.py
@@ -9,6 +9,7 @@ import matplotlib
 import numpy as np
 from tqdm import tqdm
 import os
+import time
 
 # 设置matplotlib支持中文显示
 plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']  # 指定默认字体
@@ -219,6 +220,7 @@ if __name__ == '__main__':
     best_model_path = '../model/02/best_food_model.pth'
 
     print("开始训练...")
+    start_time = time.time()  # 记录训练开始时间
     for epoch in range(num_epochs):
         print(f'\nEpoch {epoch+1}/{num_epochs}')
         print('-' * 50)
@@ -248,6 +250,14 @@ if __name__ == '__main__':
             torch.save(model.state_dict(), best_model_path)
             print(f'保存最佳模型，验证准确率: {best_val_acc:.2f}%')
 
+    end_time = time.time()  # 记录训练结束时间
+    training_duration = end_time - start_time  # 计算训练时长
+    
+    # 将秒转换为小时、分钟和秒
+    hours = int(training_duration // 3600)
+    minutes = int((training_duration % 3600) // 60)
+    seconds = int(training_duration % 60)
+    
     print(f'\n训练完成！最佳验证准确率: {best_val_acc:.2f}%')
 
     # 加载最佳模型进行测试
@@ -277,11 +287,12 @@ if __name__ == '__main__':
     plt.grid(True)
 
     plt.tight_layout()
-    plt.savefig('training_curves.png', dpi=300, bbox_inches='tight')
+    plt.savefig('../model/02/training_curves.png', dpi=300, bbox_inches='tight')
     plt.show()
 
     print(f'\n最终结果:')
     print(f'最佳验证准确率: {best_val_acc:.2f}%')
     print(f'测试准确率: {test_acc:.2f}%')
     print(f'模型已保存为: {best_model_path}')
-    print(f'训练曲线已保存为: training_curves.png')
\ No newline at end of file
+    print(f'训练曲线已保存为: training_curves.png')
+    print(f'训练时长: {hours}小时 {minutes}分钟 {seconds}秒')