 This study introduces a new approach for evaluating the quality of rock masses in hydropower projects. The KPSO algorithm is used to cluster the data into five clusters, which are then evaluated using five different criteria. These criteria include uniaxial compression strength, discontinuity spacing, RQD, and integrity coefficient. The results of the KPSO clustering are compared to those of the BQ classification method, and the extension evaluation method, both of which have been commonly used in the past. The results show that the KPSO clustering is similar to the results of the two other methods, indicating that it is reliable and accurate. Additionally, the results are consistent with the field observations, suggesting that the KPSO clustering is a valid way to evaluate the quality of rock masses in hydropower projects. This article was authored by Yun Kai-Wan, Jin Zi-Chen, Zhou Mo-Fan, and others. We are article.tv, links in the description below.