In IDRID dataset, the sensitivity and specificity were 95.65% and 91.17%, respectively, and the quadratic weighted agreement test coefficient was 90.38%. In the Kaggle competition dataset, the accuracy rate is 84.41%, and the area under the receiver operating characteristic curve was 90.36%. The experimental results show that the algorithm in this paper has certain application value in the field of DR. In view of the shortcomings of the above model, the next key task is to streamline the network model and further improve the model performance as much as possible.Diabetic Retinopathy (DR) is a prevalent acute stage of diabetes mellitus that causes vision-effecting abnormalities on the retina. In view of the difficulty in identifying the lesion area in retinal fundus images and the low grading efficiency, this paper proposes an algorithm based on multi-feature fusion of attention mechanism to diagnose and grade DR. Firstly, morphological preprocessing such as Gaussian filtering is applied to the input image to improve the feature contrast of the fundus image. Secondly, the ResNeSt50 residual network is used as the backbone of the model, and a multi-scale feature enhancement module is introduced to enhance the feature of the lesion area of ??the retinopathy image to improve the classification accuracy. Then, the graphic feature fusion module is used to fuse the enhanced local features of the main output. Finally, a weighted loss function combining center loss and focal loss is used to further improve the classification effect. In the Indian Diabetic Retinopathy (IDRID) dataset, the sensitivity and specificity were 95.65% and 91.17%, respectively, and the quadratic weighted agreement test coefficient was 90.38%. In the Kaggle competition dataset, the accuracy rate is 84.41%, and the area under the receiver operating characteristic curve was 90.36%. Simulation experiments show that the proposed algorithm has certain application value in the grading of diabetic retinopathy.