In order to determine the classification of rock mass quality more reasonably，PCA-RF classification model of rock mass quality was proposed which combined with principal component analysis and random forest algorithm.Five classification indexes were chosen which can fully reflect the rock mass quality category.The correlation analysis of indexes was calculated by principal component analysis，and three principal components were abtained by accumulated variance devoted rate，which can eliminate the correlation between each index and reduce the inputs of model.Then，the classification of rock mass quality was determined by random forest model.Twenty sets field data were chosen as training samples，and ten sets field data were chosen as testing samples.The generalization errors were estimated by cross-validation method.The results show that the classification results satisfyingly agree with the actual results at the average accuracy of 96.7%，and the probability distribution of classifications that can reflect the complexity of rock mass quality was calculated simultaneously，which can provide more detailed reference for engineering construction.