Unlike traditional 2D RGB imaging， 4D light field imaging captures the scene from multiple angular and carries its own geometric information. This feature is expected to solve the problem of underwater imaging. We propose a degradation information-guided underwater 4D light field image enhancement and angular reconstruction network based on the angular properties of 4D light field images. The network learns the degradation information of underwater images from different angular views after downsampling. It converts the degradation information into a convolution kernel to be passed to the original-size underwater light field image， realizing efficient exchange of degradation information between underwater images of different angular views. By fully using the degradation information and spatial-angular information of the underwater light field image， the network proposed in this paper can better complete the image enhancement and angular reconstruction of the underwater light field. Meanwhile， this paper proposes the spatial-angular aggregation convolution for the light field characteristics， which efficiently learns the correlation of texture information between different views by calculating the gradient difference between the centre pixel and other view pixels. The effectiveness of the network design is fully verified through quantitative experiments as well as qualitative experiments.