We propose a convolutional neural network defogging algorithm based on multi-feature fusion to overcome the problem of manual feature extraction, low contrast, and low signal-to-noise ratio in traditional defogging algorithms. The convolution neural network simulates the human visual system to hierarchically process the fog images and automatically extract image features. The algorithm adopts a learning method of the direct mapping from the hazing image to the clear defogging image, which includes feature extraction, multi-scale feature fusion, and shallow and deep feature fusion. Multi-scale feature fusion helps to rebuild details of the image. Shallow and deep feature fusion combines the contour information obtained by shallow convolution with the detail information obtained by deep convolution to enhance the overall effect of defogging. The experimental results show that the peak signal to noise ratio of the multi-feature fusion network increases by 1.280 dB compared with the single-scale network. The proposed algorithm has obvious defogging effect on natural fog image and superior detail information and contrast compared with other algorithms, which provides a new idea for defogging methods.