In order to realize the rapid and accurate identification and classification of apple leaf diseases, a variety of neural network models based on transfer learning were studied, and the accuracy of different models in apple leaf diseases identification was compared. VGG16, ResNet50, and Inception V3 neural network models are constructed. Four different apple leaf pictures were obtained from the PlantVillage, which are apple scab leaves, apple black rot leaves, apple rust leaves, and healthy apple leaves. The pictures are divided into a training set, a test set, and a verification set to train the model with a ratio of 8∶1∶1. The test results of VGG16, ResNet50, and Inception V3 were compared and analyzed under the same test conditions. The test accuracy of the three models in identifying apple leaf diseases using transfer learning technology reached 97.67%, 95.34%, and 100%, respectively. Compared with the model without transfer learning, transfer learning can significantly improve the convergence speed and accuracy of the model and provide a new method for common apple tree diseases.