When using traditional models for the early diagnosis of retinopathy in high-risk patients with Diabetic Nephropathy （DN）， the diagnostic accuracy is often compromised due to limited and category imbalanced retinal images of diabetic patients. To address this issue， an auxiliary diagnostic method for retinopathy based on dual-branch structure with knowledge distillation was proposed to improve the recognition capability for minority categories. Firstly， a teacher network pre-trained on large medical datasets was employed to guide the student network's learning process， transferring acquired knowledge to improve the student network's generalization ability and mitigate data scarcity. Secondly， a dual-branch structure was proposed in the student network. Branch 1 utilized a rebalancing strategy with Focal Loss function to emphasize challenging samples by adjusting loss function weights， while Branch 2 employed a Category Attention Module （CAM） to learn discriminative features for each category， preventing model bias towards majority categories. These two branches respectively promoted classifier learning and feature learning to alleviate category imbalance. Evaluated on clinically collected retinal image data， experimental results demonstrate that the proposed method achieves 1.05 and 1.53 percentage points improvements in accuracy and specificity respectively compared with Lesion-aware Attention Model （LAM） in screening tasks involving 66 cases （89 eyes） of high-risk patients with DN. The proposed method improves the recognition accuracy of DN and realizes the auxiliary diagnosis of retinal diseases.