In view of the limitations of traditional entry-type excavation stability assessment methods，this study explores the uses of novel data-driven machine learning methods to establish the excavation stability prediction models based on the random forest（RF） and K-nearest neighbor（KNN） methods. The proposed methods are based on 399 case histories from eight Canadian mines，covering a wide range of rock mass rating（RMR） and span，with stable，potential unstable and unstable cases categorized into ternary and binary groups. A ten-fold cross-validation method is applied to optimize the hyper-parameters during modeling. These two machine learning methods can capture the complex relationship between the excavation stability with RMR value and span without any assumptions of the underlying relationship. The results indicate that the accuracy of the binary classification results are slightly better than the ternary prediction results. For the binary classification circumstance，the accuracy and recall rate of both algorithms are higher than 90%，and the performance of the KNN algorithm is better than that of the RF algorithm. Meanwhile，the two proposed methods greatly improve the accuracy rate over previous studies and provide a reliable way for excavation stability assessment.