Discontinuity recognition is important for the description of rock discontinuities. The accuracy and efficiency of traditional recognition methods based on 3D point clouds are often affected by manual parameter settings during the process. This paper proposes a deep learning intelligent discontinuity recognition method based on a fast, maximum consistent color mapping of the point cloud. The main idea is to quickly assign different colors to different discontinuity planes according to the normal vector while keeping the color within the same discontinuity plane as uniform as possible. Then, the recognition of discontinuities in 3D point clouds is converted to the recognition of discontinuities with different colors in 2D images. Sharp points of discontinuities are also detected to facilitate the recognition. The Mask R-CNN neural network is used to train and recognize discontinuities in 2D images. Finally, the recognized 2D discontinuities are mapped back to the 3D point cloud. The results show the proposed method can achieve intelligent recognition of rock discontinuity with high efficiency and full automation without manual intervention during the process, and the recognition effect is more consistent with manual judgment than traditional methods.