Due to the light absorption and scattering effect underwater, the quality of underwater images is reduced, which greatly limits the visual range of underwater images. The robustness and accuracy of complex underwater scenes make feature extraction and matching a challenging task. In order to get better registration of underwater images, this paper proposes an underwater image feature registration method based on improved CNN-RANSAC. Firstly, the underwater image is enhanced by the image enhancement algorithm based on deep convolutional neural network, and the VGGNet-16 framework is trained by transfer learning through the underwater image classification datasets, and the improved framework is applied to carry out feature extraction and generate robust multi-scale characteristic descriptor and feature points. After coarse feature matching and dynamic interior point selection, the improved RANSAC algorithm is used to eliminate false matching points. The feature extraction and feature matching experiments are carried out on a large number of underwater image datasets. Compared with the traditional SIFT and SURF registration algorithms, the proposed algorithm can detect more feature points and achieve a significant improvement in matching accuracy.