CA-RepVGG can be used in clinical practice. The simplicity of the model and the small amount of calculation ensure the feasibility and reliability of CA-RepVGG. In this paper, CA-RepVGG is used to test and evaluate the classification effect of DR images in two datasets. At the same time, VGG-16, Inception-V3, ResNet-50, and ResNext-50 are compared with our model, and the accuracy, precision, and sensitivity of the network demonstrate the advanced nature of our model. The experimental results show that the model is not only feasible but also superior in classification. In the future, if our proposed model is applied to clinical practice, it can enhance the diagnostic efficiency of professional ophthalmologists regarding ophthalmic diseases, especially in remote and poor areas, ensuring that more patients can be treated in time and avoid losing their eyesight. If more datasets can be used to train the model in the future, the accuracy of automatic classification can be further enhanced and better results can be achieved in clinical practice.