Optic neuritis is an eye nerve disease that causes acute optic nerve injury in children and adults, and there is a risk of blindness in severe cases. Therefore, early detection and diagnosis of optic neuritis is of great help to the recovery of patients. Based on the fact that the characteristics of retinal image lesions of optic neuritis are not obvious and the classification of artificial diagnosis is difficult, with low accuracy, an improved hybrid attention mechanism CS-CBAM module is designed in this study, and it is integrated into the improved AlexNet network to form a deeper AlexNet2_att optic neuritis classification model, so as to realize the automatic classification of optic neuritis images. First, the retinal images in the dataset are preprocessed through image size adjustment, removal of image redundancy information, histogram equalization, and data enhancement. Then, based on the AlexNet network, the batch normalization layer is introduced to improve the training speed, and then the proposed hybrid attention mechanism CS-CBAM is integrated into the improved AlexNet network to form an AlexNet2_att model. Finally, the clinical data from the Third People’s Hospital of Dalian are used to evaluate the performance of the network, and the experimental results show that the classification accuracy of the model can reach 99.19%, which proves that the model has excellent practicability and robustness and high practical value, so it can assist doctors in the classification and diagnosis of optic neuritis.