To address the degradation issues in underwater images, such as color distortion, contrast reduction, and detail blurring, we propose an underwater image enhancement algorithm based on color prior guidance and attention mechanisms. We designed a Bayesian posterior feature extraction module to fuse color-prior-corrected image with the original image, and a color prior guided attention Transformer to utilize the prior information to guide the image enhancement process, which reduces the burden of color restoration during training. We constructed a hybrid multi-scale attention module to enhance feature representation in key regions. A dual-axis decoupled attention module was introduced to eliminate feature redundancy at the bottleneck layer, suppress overfitting, and improve the detail restoration. The proposed algorithm achieves PSNR/SSIM scores of 24.33/0.910 9 on UIEB, 28.40/0.885 9 on UFO, and 29.00/0.899 ?1 on EUVP, demonstrating superior performance compared to state-of-the-art approaches on all three benchmarks. In addition, the proposed algorithm improves the UIQM and UCIQE metrics by 12.81% and 5.19%, respectively, compared to the original images. These results verify the effectiveness of the proposed algorithm in improving image sharpness, structural similarity, and overall visual quality.