Underwater images are an important carrier of marine information. High-quality and clear underwater images are an important guarantee for a series of underwater operations such as marine resource exploration and marine safety monitoring. Underwater images will experience quality degradation due to factors such as selective absorption and scattering of light. In view of this, an underwater image enhancement network model based on deep multi-prior learning is proposed. First, four variants of underwater images are obtained under the prior guidance of the underwater optical imaging physical model, and a separate feature processing module containing five U-Net network structures is used to learn five private feature maps; then,the up-sampling feature maps from each U-Net structure are extracted, and through a joint feature processing module, a public feature map is learned; finally, the feature fusion module is used to uniformly represent the private feature map and the public feature map to generate an enhanced underwater image. Experimental results show that compared with various underwater image enhancement network models, the proposed model is more effective in enhancing underwater image quality. It has achieved excellent performance in multiple quality evaluation indicators.