In recent years, diabetic retinopathy (DR) has become the main reason for the global blind population increase. The early DR severity classification is particularly important to prevent vision loss in DR patients. As the number of diabetes patients grows year by year, the demand for DR grading is also rising. However, the traditional manual grading cannot meet the growing demands, and it is time-consuming and laborious. The development of deep learning technology provides a more efficient and reliable means for DR detection and grading. Although the current DR binary detection has yielded good results, DR severity grading is still challenging due to the slight differences between DR complexity and lesion degree. This work studies and summarizes DR grading methods in recent years. It introduces six deep learning classification methods based on VGG, InceptionNet, ResNet, EfficientNet, DenseNet, and CapsNet models. In addition, the study presents DR grading methods based on multi-network fusion. Finally, summary and prospect are provided for the research trends of DR grading methods based on deep learning.