Objective To automatically extract the characteristics of fundus cataract by deep learning, construct a automatic classifier for cataract, and visualize the layer-by-layer feature transformation process of the intermediate layer of deep network.Methods Based on the clinical fundus image, a deep convolutional neural network (CNN) was used to directly learn useful features from the original representation of input data, and then the features extracted by the CNN were compared with pre-defined features. The deconvolution neural network (DN) method was used to quantitatively analyze the characteristics of each intermediate layer of CNN, analyze the pixel sets that have the most contribution to the prediction performance of CNN in the input image, and explore the process in characterizing cataract by CNN.Results The classifier constructed by deep learning achieved an average accuracy of 0.818 6 in four-category tasks. Compared with the existing pre-defined feature set, the feature set automatically extracted by the deep CNN performed better in representing characteristics of cataract. The features of the intermediate layer of CNN hierarchically transformed from low-level abstraction to high-level abstraction, including changed from gradient to edge, then to the combination of edge-like divergent structures, and finally to the high-level abstraction of blood vessel and optic disc information, and this transformation process coincided with the clinical diagnostic criteria of cataract.Conclusion The classifier based on deep learning is superior to the existing classifier in terms of performance. In addition, this method has potential application in detecting other eye diseases.