A generative adversarial network model based on color correction preprocessing and an attention mechanism is proposed to address the challenges of image degradation and color shift in underwater imaging owing to scattering and absorption. In the preprocessing stage, an unsupervised image color enhancement module (UCM) is implemented to correct the color distortion of underwater images. The UCM is introduced to preprocess and enhance underwater image enhancement benchmark (UIEB) dataset, which effectively mitigates the issue of color shift. Then, in the generating adversarial network architecture, channel and spatial dual attention modules are used for feature attention of the network, thereby optimizing the ability of the image generation network for learning underwater image characteristics. Furthermore, a composite loss function is designed in the generative adversarial network to improve the performance of the generator and discriminator. Finally, enhanced images are obtained through training and testing on the underwater dataset. Compared with traditional underwater image enhancement algorithms, the proposed algorithm demonstrates better visual effects in subjective evaluations. Objective indicators show substantial improvements in the UIEB test set, with the peak signal-to-noise ratio, structural similarity index, and underwater image quality index increasing by 12%, 44%, and 12%, respectively, compared with the best-performing referenced algorithm. Experimental results show that the proposed algorithm can effectively correct color deviations in underwater images and improve image quality.