Optical coherence tomography (OCT) is a new type of ophthalmic diagnosis method with non-contact, high resolution, and other characteristics, which has been used as an important reference for doctors to clinically diagnose ophthalmic diseases. As early detection and clinical diagnosis of retinopathy are crucial, it is necessary to change the time-consuming and laborious status quo of the manual classification of diseases. To this end, this study proposes a multi-classification recognition method for retinal OCT images based on an improved MobileNetV2 neural network. This method uses feature fusion technology to process images and designs an attention increase mechanism to improve the network model, greatly improving the classification accuracy of OCT images. Compared with the original algorithm, the classification effect has been significantly improved, and the classification accuracy, recall value, accuracy, and F1 value of the proposed model reach 98.3%, 98.44%, 98.94% and 98.69%, respectively, which has exceeded the accuracy of manual classification. Such methods not only speed up the diagnostic process, reduce the burden on doctors, and improve the quality of diagnosis in actual diagnosis, but also provide a new direction for ophthalmic medical research.