The invention provides a mass image sorting system based on deep character learning. The mass sorting system comprises the following steps that firstly, non-label image data and label image data are input, the non-label image data are pre-processed, interference information is removed, and key information is remained; secondly, K-means character learning is carried out on pre-processed images, and a dictionary of the layer is obtained; thirdly, if the layer is the Nth layer, character mapping is carried out on the dictionary of the layer and the label image data, the fifth step is executed after the deep characters are obtained, or otherwise, character mapping is carried out on the dictionary of the layer and the non-label image data, and the deep characters are obtained; fourthly, the high correlation characters are combined into a receptive field according to the correlation of the deep characters, if the layer is the (N-1)th layer, the fifth step is executed, or otherwise the layer serves as the input information of the next layer and is sent to the second step; fifthly, in the Nth layer, the learned characters are input to an SVM sorter, and sorting is carried out.