Accurate acquisition of the orientation and spatial distribution of rock mass discontinuities is essential for the stability analysis of high and steep rock slopes. Conventional clustering-based methods

which rely heavily on point cloud normal vectors

often suffer from significant identification errors when abrupt variations exist in the point cloud data. To address this limitation

a discontinuity mapping approach integrating geometric and signal features of point cloud is developed

improving both the accuracy and robustness of the identification result. Furthermore

the k-means++ algorithm is enhanced by refining its decision-making process and optimizing cluster center selection

enabling more effective segmentation and information extraction of rock mass discontinuities. The proposed method is applied to a rock slope in Zhejiang

China. The results demonstrate that it can effectively identify discontinuities from point cloud data

accurately extract their orientations

and perform dominant set classification. Comparative analysis with the DBSCAN algorithm further verifies the effectiveness of the proposed approach. Validation against manual measurements shows that the errors in dip direction and dip angle are within 3° and 4°

respectively

indicating that the method provides accurate and reliable results with strong engineering applicability.