In order to solve the serious color bias and low contrast quality problems caused by light absorption and scattering, an underwater image enhancement method combining lightweight feature fusion network and multi-color model correction is proposed in this paper. Firstly, the feature fusion network of the encoder and decoder structure of the convolution layer is used to correct the color deviation of the underwater image. The improved feature fusion module in the network reduces the damage of the fully connected layer to the image spatial structure, protects the spatial features, and reduces the number of parameters of the module. At the same time, the improved attention module parallelizes the pooling operation to extract texture details and protect background information. Then, the multi-color model correction module is used to correct according to the relationship between pixels to further reduce the color deviation and improve the contrast and brightness.The experimental results show that compared with the latest image enhancement methods, the average value of NRMSE, PSNR and SSIM on the reference image dataset is improved by 9.30%, 3.70% and 2.30% than the second place of comparison algorithms, respectively. The average value of UCIQE, IE and NIQE on the non-reference image dataset is 6%, 2.9% and 4.5% higher than the second place of comparison algorithms. Combining subjective perception and objective evaluation, this method can correct color deviation of underwater images, improve contrast and brightness, and improve image quality.