Aiming at the degradation of the visual quality of underwater images and the low feature utilization of a single convolutional layer, an underwater image enhancement model based on multi-scale feature extraction is proposed. The model uses the improved multi-scale feature extraction module to extract underwater image features based on the convolutional neural network. First, a separable residual dense block (SRDB) is constructed as the basic feature extraction unit, and the SRDB module is used for residual dense connection to obtain multi-level feature information, and finally merge the multi-level feature information under three different initial receptive fields as the output of this module. Multiple sets of experimental results show that the enhanced underwater image of the model effectively improves the color distortion and low contrast while maintaining rich edge details; The PSNR and SSIM on the EUVP test set rose to 28.52 and 0.88, respectively, and the UIQM and NIQE on the real pufferfish image test set rose to 2.84 and 5.95, respectively, outperforming the comparison methods. Research shows that this model has a high FPS, which greatly improves the visual perception quality of underwater images while maintaining good real-time performance.