Abstract: Fundus is the only part where the arteries, veins and capillaries can be observed with the naked eyes directly and centrally. Therefore, the fundus image is an important basis for doctor to diagnose fundus diseases and some other diseases such as diabetes, hypertension, and hyperlipidemia. High quality fundus images are the premise for doctors to analyze and treat the fundus diseases for patients. Classification of fundus image quality based on conditions of fundus structure clarity and image contrast in retinal images collected by fundus cameras has become a difficult problem with both research value and challenge. Firstly, the research significance and practical value of fundus image quality classification are briefly described, and its development history is reviewed. Secondly, the method classification and the basic idea of each method are introduced and the representative algorithms and their characteristics in various methods are introduced. Thirdly, the data set for fundus image quality classification is introduced, and the performance of the main fundus image quality classification methods is analyzed and compared. The analysis shows that among the traditional method, it is more objective to judge the quality of retinal image based on the characteristics of fundus structure than general image quality parameters. With the emergence of neural network and machine learning, the quality classification method based on convolutional neural network driven by big data has better performance in accuracy and robustness. Finally, the future development trend of fundus image quality classification is prospected.