Multi-category ordered-dependent-variable Logistic regression model was introduced into the rock mass classification, rock uniaxial compressive strength, rock acoustic wave velocity, intensity of jointing, joint roughness coefficient, weathering variation coefficient of joint surface and permeability coefficient were used as independent variables, rock mass level was used as dependent variable, rock mass samples data was used to establish rock mass classification formula. Goodness of fit, model validity and predictive ability test were carried out to evaluate the correctness of the model, the results show that: Logistic regression analysis model has good performance, misjudging rate of training samples is zero, the predictive ability is strong. Compared to the distance discriminant analysis model, regression analysis model of rock mass classification is more convenient in use at site and has lower misjudging rate; besides, it can output the probability of all levels that rock mass belong to, which provide additional information of rock mass to engineering designer; compared to ordinary regression analysis, multi-category ordered-dependent-variable Logistic regression is more appropriate to analyze rock mass classification problems due to their discrete ordered dependent variables, thus multi-category ordered-dependent-variable regression model is a more superior rock mass classification method.