## 1 Introduction  
With the development of hydropower energy, the rock mass problems encountered when constructing hydropower stations have become increasingly complex. By evaluating rock mass quality, rock mass engineering can be reasonably designed based on the project quality. In evaluating the geological engineering conditions and obtaining the engineering characteristics of rock mass, the evaluation and classification of rock mass quality are critical. The quality of dam abutment rock mass is related to the safety, stability, and economic benefits of hydropower projects. Thus, it is necessary to reasonably evaluate the quality of rock. The results of the study on the quality of engineering rock mass, from the rock mass rating (RMR) method and tunneling quality index (Q) classification, have been presented in previous studies ([PERSON], 1974; [PERSON], 1973,1989) [1; 2; 3]. [PERSON] reviewed the rock mass rating and tunnel construction quality index system of tunnel design, includingdevelopment, improvement, application and limitations [4]. [PERSON] discussed the application of Q classification and RMR classification in tunnel rock mass [5; 6]. To date, many researchers have carried out significant research work. At the same time, the national ministries and commissions have also proposed classification methods for evaluating rock mass quality according to specific conditions. These classification methods achieved good results in practical engineering applications. In most previous studies, qualitative or quantitative methods have been used to evaluate rock mass quality. The main considerations were rock strength, the development of structural planes, the geological environment in which the rock mass occurs, and certain other factors. Many methods are combined with these factors, such as the sum-difference method, cumulative quotient method or their combination. However, these methods have many shortcomings, which need to be further developed and improved. Many system theories have been introduced for rock mass quality evaluation. Extension theory is a widely used nonlinear method [7; 8; 9; 10]. In addition, there unaccertained measure theory [11], rough set and artificial neural network [12], reduction concept lattice, and fuzzy optimization are used [13]. The existing research methods only consider the influence of one of the factors at a time. Also, the geological conditions of the rock mass are very complex. Therefore, using previous research methods is not appropriate when considering the influence of only one factor alone. Moreover, the study of the rock mass quality problem [14; 15] is a very complex contradictory problem, and the results of different index evaluations may differ greatly. Data mining plays an important role in the computer field. It automatically searches the process of special relational information hidden in a large number of data and obtains the best information. Clustering analysis is an effective data mining algorithm. The K-PSO clustering algorithm is an optimized K-means algorithm based on the particle swarm optimization (PSO) algorithm [16; 17; 18; 19], which has the characteristics of stable convergence and high computational efficiency. The PSO algorithm is a new optimization technology, whose idea comes from artificial life and evolutionary computation theory. PSO completes the optimization by following the optimal solution found by the particle and the optimal solution of the whole group. The algorithm is simple and easy to implement, with few adjustable parameters, and has been widely studied and applied. In this paper, the K-means algorithm of particle swarm optimization (KPSO) is used to evaluate and analyze the rock mass quality classification in the study area. Compared with the traditional classification method, the new method is not sensitive to the initial clustering center, and overcomes the shortcomings of the difficulty in selecting the initial clustering center. In addition, the PSO algorithm is a global optimization method.