The dominant grouping of rock mass structural plane occurrence is of great significance to reveal the distribution and characteristics of different types of structural plane. The traditional grouping method of structural plane pole density map usually relies on geological experience and lacks some objectivity. Therefore, the mean shift clustering algorithm is introduced in this paper to study the predominance grouping of rock mass structural plane occurrence. Firstly, the occurrence data of rock mass structural plane with different degrees of dispersion are generated manually. Then, the generated occurrence data is converted into coordinates in three-dimensional space, and the sinusoidal value γ of the unit normal vector is used as the similarity measure. Next, the mean shift algorithm is used to perform cluster analysis on the measured data set. Compared with the traditional pole density map method and K-means clustering algorithm, the validity test index and clustering error recognition rate are close to the K-means clustering algorithm. Finally, taking the Chongqing Sangong ore-rock slope as an engineering example, the rationality and effectiveness of the new method are verified by the field data collected from the structural plane. The results show that the clustering effect of the proposed method is superior to the traditional pole map grouping method and K-means clustering algorithm. The clustering results are objective and reasonable, and the clustering effect for near-horizontal occurrence is also good.