For Diabetic Retinopathy (DR), the image resolution is too high, the lesion features are too scattered to obtain, and the positive, negative, hard and easy samples are imbalanced, thus the DR staging accuracy cannot be effectively improved. Therefore, a DR staging method based on the combination of improved Faster Region-based Convolutional Neural Network (Faster R-CNN) and subgraph segmentation was proposed. First, subgraph segmentation was used to solve the interference problem of the optic disc region to lesion recognition. Second, a deep residual network was used in the feature extraction process to solve the problem of difficulty of obtaining features due to the small proportion of the lesions in the high-resolution fundus image. Finally, the Online Hard Example Mining (OHEM) method was used to solve the problem of imbalance between positive, negative, hard and easy samples during the generation of Region of Interest (ROI). In the DR staging experiments on EyePACS, an internationally open dataset, the accuracy of the proposed method in DR staging reached 94.83% in stage 0, 86.84% in stage 1, 94.00% in stage 2, 87.21% in stage 3 and 82.96% in phase 4. Experimental results show that the improved Faster R-CNN can efficiently stage DR images and automatically label the lesions.