Diabetes retinopathy (DR) is a kind of diabetes microvascular disease, and it will be reflected in the bulbar conjunctival microvessels. Images of bulbar conjunctival vessels are easier to obtain than fundus images,but the characteristic changes of microvessels are subtle and difficult to quantify. In order to enable early auxiliary diagnosis of patients,according to the association between the morphology of bulbar conjunctiva microvessels and DR, this paper first preprocesses the bulbar conjunctiva images,enhances the image using the contrast limited adaptive histogram equalization (CLAHE) algorithm,and enhances the data with random processing.Then CTCNet is constructed by combining the advantages of convolutional neural network (CNN) and Transformer, and the processed images of bulbar conjunctival vessels are classified into DR.The classification accuracy reaches 97.44%,sensitivity 97.69%,specificity 97.11%,and accuracy 97.69%.Through experimental comparison between CNN and Transformer,CTCNet has better performance than other models and can effectively identify DR.