Rock mass structure mainly comprises rock discontinuities, which control rock mass stability. In this study, a new similarity measurement method for obtaining rock mass point cloud data using three-dimensional (3D) laser scanning technology is proposed to address the limitation of traditional similarity metrics in expressing the similarity of rock point clouds. This method comprehensively represents the spatial position and directional differences between data points, thus reducing the similarity between points on different discontinuities. Using this similarity measurement as the clustering criterion for the DBSCAN algorithm, clustering of rock mass point clouds was performed with dynamic clustering parameters. This process yielded point cloud data for individual discontinuities. Subsequently, the clustering results were corrected, enabling intelligent identification of rock discontinuities. The method was applied to point cloud data from sedimentary rock outcrops, and the results were compared with those obtained using a discontinuities occurrence information comparison algorithm. The findings reveal that the results from the two methods are largely consistent, with the maximum deviation in occurrence being within 2°. This meets the requirements for engineering applications, providing a theoretical foundation and practical reference for similar applications.