Abstract: To address the difficulties in parameter determination of in current mainstream point cloud analysis methods for discontinuity identification, an improved rock mass discontinuity identification method based on an improved Density Peak Clustering (DPC) algorithm is proposed. First, neighboring points are searched to estimate point cloud curvature and normal vectors, followed by the filtering of high-curvature edge points. Second, a dual strategy of data sampling and data space gridding is employed to reduce the computational complexity of the DPC algorithm, on which Gaussian kernel density estimation and density-distance calculation are performed on grid points. Third, the optimal Gaussian kernel bandwidth and the optimal number of clusters are determined through cross-validation and exhaustive search, and clustering labels are assigned to all points based on the sine-squared distance. Finally, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and Principal Component Analysis (PCA) are employed to adaptively segment discontinuities and to determine their orientations. The reliability of the proposed method is validated using regular-shaped point cloud and rock slope point clouds from the publicly available Rockbench repository, and the method was further successfully applied to a hazardous rock cut slope in China. The results demonstrate that, compared with DSE, the proposed method achieves a maximum improvement in computational efficiency of approximately 42-fold, while maintaining identification accuracy within 2°. The research outcomes provide an objective and efficient intelligent measurement tool for large-scale discontinuity surveys in engineering practice.