The use of Optical Coherence Tomography (OCT) to produce retinal disease images is an important measure to classify ophthalmic diseases. The purpose of this study is to use the transfer learning method of four different classification models to automatically classify the OCT retinal images of diabetic macular edema, age-related macular degeneration, and drusen to realize the application of transfer learning in OCT image classification. After pre-training the four neural network models VGG-16, Inception V3, MobileNet-V2, ShuffleNet-V2 on the large-scale graph classification data set, fine-tunieg the model and update the training parameters to find the realization of the above three ophthalmic diseases. The optimal model of automatic classification achieves efficient OCT retinopathy classification effect. The experimental results show that the lightweight MobileNet-V2 of the four models has better evaluation indicators than other models after the model is fine-tuned.