Diabetic retinopathy (DR) is currently one of the leading blinding diseases in humans. Aiming at the problems of small differences between samples and uneven class distribution in DR datasets, which restrict the improvement of grading performance, this paper proposes a classification algorithm for the fusion of attention linear features diversification (FALFD). Firstly, the improved Res2Net residual network is used as the model backbone to increase the receptive field, and further improve the ability of the network to capture feature information. Secondly, the adaptive feature diversification module (AFDM) is introduced to identify the tiny pathological features that can be resolved in the fundus images, and local features with high semantic information are obtained, which avoids the limitation of a single feature region and improves the classification accuracy. Then, the bilinear attention fusion module (BAFM) is used to increase the proportion of network weights that can identify regional features. Finally, the regularized focal loss (FL) is used to further improve the classification performance of the algorithm. On the IDRID dataset, the sensitivity and specificity are 94.20% and 97.05%, and the quadratic weighting coefficient is 87.83%, respectively. On the APTOS 2019 dataset, the quadratic weighting coefficient and the area under the receiver operating curve are 88.06% and 93.90%, respectively. The experimental results show that the algorithm has some value in the field of DR classification.