Abstract: The integrity of rock mass is an important parameter in evaluating the quality grade of hydraulic rock mass. The traditional methods often adopt a single index that cannot fully reflect the comprehensive influences of geological conditions such as structural plane, groundwater, and unloading on the evaluation results. An intelligent evaluation method for the integrity of hydraulic rock mass is proposed by coupling with multi-source survey information. Firstly, the synthetic minority oversampling (SMOTE) algorithm is used to balance the survey information data to improve the data set structure. Then the random forest algorithm is used to predict the original rock mass integrity data and the pre-processed data, respectively. Based on the data of actual projects, the validity and applicability are verified, and the predicted results are discussed and analyzed according to different factors affecting the integrity of rock mass. The results show that the proposed method can effectively improve the evaluation accuracy of the integrity of a few rock samples by balancing the data sets. By coupling and mining the deep information of different integrity indexes, the intelligent evaluation of rock mass integrity can be realized, which provides a new method for further assisting the evaluation of rock mass quality.