Since the traditional single image dehazing algorithm is susceptible to the prior knowledge constraint of hazy image and color distortion, this paper proposes a multi-scale convolutional neural network (CNN) single image dehazing method based on deep learning, which realizes image dehazing by learning the mapping relationship between hazy image and atmospheric transmission. According to the hazy image forming mechanism of atmospheric scattering model, an end-to-end multi-scale full CNN model is designed. The shallow layer features of hazy image are extracted by convolution layer operation, and then the deep features are extracted by multi-scale convolution kernel in parallel. Then the shallow layer features and deep features are fused by jump connection. Finally, the non-linear regression method is used to obtain the corresponding transmission features of the hazy image. According to the atmospheric scattering model, the haze-free image is restored. The model is trained by using hazy image data sets. The experimental results show that the proposed method can achieve good dehazing effect in the experiments of synthesizing hazy images and real natural hazy images. The proposed method is superior to other contrast algorithms in subjective and objective evaluations.