Diabetic retinopathy (DR) is one of the most common complications of diabetes.Due to the small differences between inter classes and complex features of retinal image lesions,the traditional deep learning network has some problems,such as non-focusing on lesion points and inadequate feature extraction.In view of above problems,an automatic classification model for retinopathy,D-SCNet,is proposed.Firstly,a global attention module SC is designed,which alternately arranges in a spatial and channel manner and promotes each other,thereby overcoming the traditional two-dimensional independent attention mode where the global attention module first channels and then space.Then the high-efficiency activation function EReLU is used for nonlinear processing to increase the attention to the lesions numbers in the yellow rings,bright red patches and the optic disc areas.Finally,with lightweight DenseNet121 as the main backbone network,the attention module SC is placed after 3×3 convolution of the bottleneck structure to obtain a new feature extraction bottleneck structure,which promotes the network to extract effective information and improve classification accuracy.The test results show that the SC attention module has a certain generalization effect,and the classification accuracy on traditional typical networks Vgg,ResNet,and Xception is improved by 3.04%,0.76%,and 2.48% respectively.Meanwhile,the D-SCNet model has some advantages over the existing models.If this model is applied in clinical practice,it can assist ophthalmologists in conducting primary screening for retinopathy.