It is difficult to judge the image multiply distortion type. In order to solve the problem， based on the idea of deep learning multi-label classification， a new multiply distortion type judgement method based on multi-scale and multi-classifier Convolutional Neural Network （CNN） was proposed. Firstly， the image block containing high-frequency information was obtained from the image， and the image block was input into the convolution layers of different receptive fields to extract the shallow feature maps of the image. Then， the shallow feature maps were input into the structure of each sub-classifier for deep feature extraction and fusion， and the fused features were judged by the Sigmoid classifier. Finally， the judgment results of different sub-classifiers were fused to obtain the multiply distortion type of image. Experimental results show that， on the Natural Scene Mixed Disordered Images Database （NSMDID）， the average judgment accuracy of the proposed method can reach 91.4% for different types of multiply distortion types in the images， and most of them are above 96.8%， illustrating that the proposed method can effectively judge the types of distortion in multiply distortion images.