The identification of shale lithology is of great importance for the exploration and development of shale reservoirs. The lithology and mineralogical composition of shale are closely related, but a small number of laboratory core analysis samples are insufficient to evaluate the lithology of the entire formation. In this study, a lithology identification method using conventional logging curves is proposed for the shale stratigraphy of the Qingshankou Formation in the Gulong Depression of the Songliao Basin, northeastern China. First, a mineral pre-training model is constructed using discrete petrophysical experimental data with logging data, and features are generated for the logging data. Second, an adaptive multi-objective swarm crossover optimization method is employed to address the imbalance of logging data. Finally, the model is combined with a Bayesian gradient boosting algorithm for lithology identification. The proposed method demonstrates superior performance to eXtreme Gradient Boosting, Support Vector Machines, Multilayer Perceptron, and Random Forest in terms of accuracy, weight perspective, and macro perspective evaluation indexes. The method has been successfully applied in actual wells, with excellent results. The results indicate that the workflow is a reliable means of shale lithology identification.