To overcome the problems of poor generalizability and an inability to adapt to complex real scenes of traditional low-light image enhancement algorithms, a new method based on multi-scale concat convolutional neural network is proposed here. This method achieves low-light image enhancement by learning the mapping relationship between low-light and normal images. Taking the low-light image as input, the shallow layer information of the image is extracted through the preprocessing module. Then, Selective Kernel Network (SKNet) is fused to the local path to form a feature extraction network. Finally, the global feature is fused with the local feature, which is obtained by weight learning of the feature map with a channel attention module. Bilateral guided upsampling is used to restore the image size and obtain the mapping function of the low-light image, after which the image enhancement is completed. Based on the MIT-Adobe 5K dataset, a comparative experiment with nine other advanced methods showed that the proposed method can effectively improve the brightness and details of low-light images. Hence, the proposed method is superior to other contrast algorithms in terms of visual effects and quantitative evaluation.