Aiming at the problems of blurred details of underwater images and serious color distortion, this paper proposes a dynamic heterogeneous feature fusion underwater image enhancement network based on the autoencoder structure. First, a heterogeneous feature fusion module is designed to integrate different levels and different levels of features to improve the overall perception of detailed information and semantic information of the network. Second, a new feature attention mechanism is designed, the traditional channel attention mechanism is improved, and the improved channel attention and pixel attention mechanism is added to the heterogeneous feature fusion process to strengthen the network's ability to extract pixel features of different turbidity. Then, a dynamic feature enhancement module is designed to adaptively expand the receptive field to improve the network's adaptability to image distortion scenes and model conversion capabilities, and strengthen the network's learning of regions of interest. Finally, the color loss function is designed, and the absolute error loss and the structural similarity loss are jointly minimized, and the color cast is corrected on the basis of maintaining the image texture. The experimental results show that the proposed algorithm can effectively improve the feature extraction ability of the network, reduce the haze effect of underwater images, and improve the clarity and color saturation of the image.