This paper proposes a multiplexed fusion deep aggregate learning algorithm for underwater image enhancement. First, the image preprocessing algorithm is used to obtain the image attribute information of three branches (contrast, brightness, and colour) respectively. Then, the image attribute dependency module is designed to obtain fusion features of multiplexed using a fusion network, and then explore the potential fused image attribute correlations through parallel graph convolution. A self-attention deep aggregate learning module is introduced to deeply mine the interaction information between the private and public domains of the multiplexed using sequential self-attention and global attribute iteration mechanisms, and also effectively extract and integrate the important information between image attributes by means of aggregation bottlenecks to achieve more accurate feature representation. Finally, skip connections are introduced to continue enhancing the image output to further improve the effect of image enhancement. Numerous experiments have demonstrated that the proposed method can effectively remove colour bias and blurring, and improve image clarity, as well as facilitate underwater image segmentation and key point detection tasks. The peak signal-to-noise ratio and structural similarity metrics can reach the highest values of 23.01 dB and 0.90, which are improved by 5.0% and 4.7% compared with the suboptimal method, while the underwater colour image quality metrics and information entropy metrics have the highest values of 0.93 and 14.33, which are improved by 2.2% and 0.5% compared with the suboptimal method.