To solve the problems of color distortion and loss of details in underwater images, an underwater image enhancement algorithm based on Multi-Scale Triple Attention (MSTA) is proposed.The algorithm uses the Generative Adversarial Network (GAN) as the basic architecture, and the generative network adopts the encoding and decoding structure.An MSTA module is designed.The combination of the multi-scale structure and the Triple Attention (TA) mechanism can realize the cross-dimensional interaction of information at different levels, making the network better learn the features of underwater images and suppress the features of noise.The discriminant network adopts a structure similar to Markov discriminator.Multiple loss functions are constructed to make the generated image consistent with the reference image in terms of structure, content and color.The experimental results show that the proposed algorithm is superior to the comparison algorithms in terms of subjective visual effects and objective evaluation indicators.The proposed algorithm can effectively improve the feature extraction ability of the network, restore the color of underwater images in different scenes, and enhance the contrast and clarity of the images.