To address the problem of low recognition accuracy for identifying different apple leaf diseases, an apple leaf disease identification model was proposed based on snapshot ensemble. Firstly, the original dataset was augmented by various digital image processing methods. Then, an Inception-ResNet V2 was chosen as base model. The convolutional block attention module (CBAM) was introduced to enhance the feature extraction capability for apple leaf diseases. And focal loss was used to alleviate the imbalance of samples in each category. Finally, the model was integrated through snapshot ensemble to obtain the final identification model for different degrees of diseases on apple leaves. The image was input to the final model for identification. Compared with the original single Inception-ResNet V2, the recognition accuracy of the improved model was increased from 88.32% to 90.82%. Experimental results showed that the ensemble model had a high accuracy rate, which provided an idea and explored a approach for diseases of different degrees on apple leaves.