## 4 Conclusions  
In this paper, a new method for evaluating rock mass quality based on the K-PSO clustering algorithm is proposed. The subjective and objective weights of evaluation factors are determined by the weighted least squares method, the objective weights are determined by the entropy method, and the combined weights of each influencing factor are determined by game theory.  
1. This is based on multi-factors and multi-angles. The number and type of rock mass quality parameters are free, and can truly and objectively reflect the field investigation results. The proposed method can fully use the information obtained from engineering investigation and solve the past classification of rock mass quality deficiencies and defects, which used only fixed factors.
2. The weight of the rock mass quality evaluation factor is determined to be 0.142, 0.179, 0.035, 0.116, and 0.108 using game theory to combine the least-square method and the entropy method. This method can overcome the shortcomings of subjective and objective weighting and is suitable for dealing with complex, multi-objective decision-making problems.
3. The factors affecting the evaluation of rock mass quality are uncertain, nonlinear and fuzzy. Using a clustering model to evaluate rock mass quality can effectively solve the problem of uncertainty between factors. Especially when there is a lack of geological data, this method can quickly and reasonably analyze the rock mass quality of the tunnel section.
4. In traditional rock engineering, the methods of rock mass quality evaluation include the BQ classification method, RMR classification method, and Q classification method. Most of them use qualitative or quantitative methods to evaluate the quality of the rock mass. However, the rock mass quality problem is a complex contradictory problem, and the results of different indexes may be different. Clustering analysis is an effective data mining algorithm. The K-PSO clustering algorithm is an optimized K-Means algorithm based on the PSO algorithm, which has the characteristics of stable convergence and high computational efficiency. It can quickly and accurately classify rock mass quality and determine the evaluation grade of rock mass quality. The evaluation results are consistent with the traditional BQ classification method and the widely used extension theory evaluation results. They show that using the combination weighting method and K-PSO clustering algorithm is feasible to evaluate and construct the rock mass quality evaluation model for a hydropower station, truly reflecting the actual rock mass quality grade. The evaluation results were found to be reasonable and effective.  
Conceptualization, Y.R., J.M., R.H., W.H., Y.L. and Y.S.; Methodology, J.C., Z.F., T.W., J.M., R.H., W.L., Y.L. and Y.S.; Software, Y.R. and J.C.; Validation, Z.F.; Formal analysis, Y.R. and T.W.; Investigation, Z.F., T.W., R.H., W.H. and W.L.; Resources, J.M.; Data curation, J.C.; Writing--original draft, Y.R.; Writing--review & editing, Y.L. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 42007261, 52102422 and 42104094), Natural Science Foundation of Fujian Province, China (Grant No. 2021J05026). Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The datasets generated and analyzed in the current study may be obtained from the corresponding author upon reasonable request.  
The authors are very grateful to the editor and the anonymous reviewers for their thorough and constructive reviews.  
The authors declare no conflict of interest.