Abstract: To investigate the feature extraction effect of deep learning technology on tobacco leaf images, a tobacco leaf grading method based on a Convolutional Neural Network(CNN)model was proposed, and the tobacco leaf features involved in the model were visualized and analyzed. The local tobacco leaf images with high-resolution were obtained by image preprocessing to compensate for the loss of tobacco leaf details due to zooming the global tobacco leaf image. The modified CNN models VGG-16 and ResNet-50 were used to extract the features of the global and local tobacco leaf images respectively. A classifier was configured to classify and fuse the feature vectors of the global and local tobacco images. The Class Activation Map (CAM) method was used to draw the thermodynamic charts of the tobacco leaf features involved in the models. The results showed that the accuracy of the proposed method for grading tobacco leaves of six grades reached 84.71%, and the test time for a single tobacco leaf image was 17.87 ms. The thermodynamic charts of the features indicated that the ResNet-50 model was more sensitive to the local features of tobacco leaves, such as disease spot, wrinkle, main vein and texture trend. The proposed method provides support for the fast and accurate grading of tobacco leaves.