Aiming at the problems of color distortion and detail loss in underwater images due to water scattering and absorption， a generative adversarial network model integrating multi-scale information and attention mechanism was proposed to enhance underwater images. Firstly， to fully exploit and enhance both local and global information of the image， local encoders and global encoders were employed to extract local and global features respectively， which were then fused to achieve complementarity. Next， a multi-scale hybrid convolution was designed to capture multi-scale information， increasing the network's adaptability to features at different scales. Subsequently， attention mechanisms were utilized to enhance the accuracy of feature extraction， emphasizing the focus on high-value features. Finally， by iteratively applying multi-scale hybrid convolution and attention mechanisms to refine features， the enhanced image was gradually up-sampled. Compared with the six classical and state-of-the-art methods， the proposed model not only achieved the best visual perception in subjective evaluations but also outperformed the six comparative methods on the entire test set in terms of four objective evaluation metrics peak signal-to-noise ratio （PSNR）， structural similarity （SSIM）， underwater image quality measurement （UIQM）， and natural image quality evaluation （NIQE） with average scores of 22.499， 0.789， 2.911， and 4.175， respectively. The improvements over the best scores among the comparative methods are 0.353， 0.002， 0.025， and 0.307， respectively. These results indicate that the proposed model not only corrects image color distortion but also performs well in restoring image details， increasing image contrast， and enhancing clarity. Therefore， it shows promising prospects for practical applications in underwater image enhancement.