The JRC values of 112 rock joints were collected, including 10 Barton standard profiles, and 8 morphological parameters of each profile were calculated in view of the difficulty and low accuracy for prediction of the current joint roughness coefficient (JRC) quantitative evaluation model. Principal component analysis was used to reduce the dimension of these morphological parameters, and 5 principal components were obtained. 102 groups of profile data were used as training samples, the Boosting-decision tree (DT) C5.0 algorithm was used to build the training model, and Barton 10 standard profiles were used for model verification. The DT C5.0 model, CHAID DT model, support vector machine (SVM) model, and artificial neural network models were selected to verify the prediction accuracy of each model. Results showed that the average error and root mean square error of the Boosting-DT C5.0 model were the least. Established explicit JRC prediction model included 8 layers and 68 nodes.