The data-driven approach of machine learning enables the intelligent construction of tunnel boring machines (TBM), which is crucial for optimizing the tunneling process, improving the safety of tunneling and reducing labor costs. In this paper, to solve the problems of excessive noise, redundant parameters and difficult effective feature extraction in TBM operation data, a data-driven machine learning method is used to mine the complex machine-soil interaction contained in the data and realize the classification and prediction of TBM surrounding rock mass. Firstly, for the large amount of operation data generated during TBM excavation, feature extraction was carried out on the typical 7 excavation parameter curves by kernel density estimation (KDE) to obtain the maximum probability values of TBM key operation parameters in the stable excavation stage. Then, based on the actual TBM operation data, an integrated learning algorithm for surrounding rock classification stacking is proposed. The algorithm is further optimized through k-fold cross-validation, and the complex relationships in the data are mined by using the two-layer learning framework of base classifier and meta-classifier. Finally, a data set of 5868 TBM segments is used to verify the effectiveness of the proposed algorithm. The results show that the average F1 of the four-classification problem is 0.705, and the average F1 of the two-classification problem is 0.797, which are better than the four selected base classifiers.