Aiming at the phenomenon of retinopathy of diabetic patients, a diagnosis model of diabetic retinopathy based on deep learning is proposed. First, under the premise of ensuring the depth of image recognition model, the composition of Inception module is modified to reduce the model parameters and improve the convergence speed. Next, the residual module is introduced to solve the problems of gradient disappearance and gradient explosion caused by the increase of model depth. Last, by using the method of data expansion and setting the Dropout, the phenomenon that the model is over-fitting due to the insufficient data set is effectively avoided, thereby realizing the detection of the disease level of diabetic retinopathy. Experimental results show that the deep convolutional neural network DetectionNet proposed in this paper has a recognition rate of 91% for the classification of diabetic retinopathy. Compared with network models such as LeNet, AlexNet, and CompactNet, the proposed DetectionNet improves the recognition rate by more than 20%. This research is of great significance for the early prevention and treatment of diabetic patients and the avoidance of diabetic retinopathy.