In order to solve the problems of color distortion, key information blur and detail loss perplexed underwater image, an underwater image enhancement method based on SK attention residual network is proposed.The generator structure in the generative adversarial network is improved, and a residual module is introduced to reduce the feature loss between encoder and decoder, thus enhance the image detail and color.To make the network adapt to different scale feature maps to extract key information of images, the SK attention mechanism is added after the residual module.Meanwhile, a parametric rectified linear unit is used to improve the fitting ability of the network.This method is verified on real and synthetic underwater image datasets, and traditional method and deep learning method are used for subjective and objective evaluations.In the subjective effect analysis, it is found that the color, key information and detail features have been greatly improved in enhanced images.In the objective evaluation, it is found that the indicator values of the proposed method are higher than those of existing underwater image enhancement algorithms, which verifies the effectiveness of this method.