The identification and clustering analysis of rock discontinuities are the basis for studying the structural characteristics of rock masses and assessing the stability of rock masses. In order to perform fast and effective clustering of rock body discontinuities, a 3D point cloud-based rock body discontinuity identification and fast clustering method is proposed. Firstly, the point cloud segmentation and plane fitting are performed by FACET to extract the rock body discontinuity surface. Secondly, the local density and control distance are calculated by the similarity distance between the rock discontinuity faces, and the decision map is drawn to find the clustering center and the number of clusters automatically. Finally, according to the boundary density, the rock discontinuities are divided into core discontinuities and outlier discontinuities, and the outliers are eliminated. This method avoids the interference of human subjective factors and improves the accuracy of clustering analysis. Through the clustering analysis of cubic and hexahedral, the number of clusters is consistent with the expectation, and the average yield of each cluster is similar to the fitting results of point cloud discontinuity surface, with the maximum error of dip direction 0.47° and 1.78°, and the maximum error of dip angle 2.98° and 2.57°, respectively. At the same time, the clustering performance is improved to a certain extent compared with K-means, K-means++ and DBSCAN clustering algorithms, up to 0.834. Field application to the discontinuous surface of a high, steep cliff in Huidong County, Sichuan Province, demonstrates its effectiveness without predefined clustering centers or numbers, yielding results comparable to measured data and RocScience dips, thereby satisfying accuracy requirements and exhibiting robust performance.