Lithology identification is a key and difficult problem in reservoir geological interpretation. The development and application of artificial intelligence, especially machine learning technology, provides a new technical way to solve lithology identification problems. This paper uses machine learning models such as support vector machine (SVM), multi-grained cascade forest (GCForest), random forest (RF) and eXtreme gradient boosting (XGBoost) to build a heterogeneous multi-layer integrated learning model. The integrated learning model overcomes the shortcomings of single model such as high requirement for data sets, poor generalization ability and low recognition accuracy. In this paper, lithology recognition experiments are carried out using integrated models and single models. The experimental results show that the average accuracy of the integrated model is 96.66%, higher than that of SVM (75.53%), GCForest (96.21%), random forest (95.06%) and XGBoost (95.77%). The integrated model can be effectively applied to lithology identification and classification tasks in reservoir geological analysis with strong adaptability and high recognition accuracy.