Abstract：To improve the accuracy of lithology identification in uranium mine borehole strata and addressthe limitations of traditional ensemble learning models in achieving satisfactory lithology recognitionperformance, this study proposes a PCA-optimized Stacking ensemble learning model. The methodology beginsby quantitatively evaluating the linear correlation strength between various logging parameters and the targetlithology using Pearson correlation coefficients. This statistical analysis is further refined by incorporatinggeophysical mechanisms of well logging, leading to the selection of six logging parameters that demonstratestrong lithological relevance as input features. These parameters effectively capture essential petrophysicalcharacteristics while minimizing redundancy in the feature set. Simultaneously, the model employs a dual-facetedapproach to base learner selection. The Pearson correlation coefficients of prediction errors among base learnersare systematically calculated to assess their error interdependence. Additionally, the Q-statistic matrix is utilizedto evaluate the correlation and diversity of prediction outcomes. From this, a combination of base models withstrong error complementarity (i.e., low correlation) and significant prediction mode differences (i.e., low Q-value)is selected. To optimally consolidate the predictive information, the PCA algorithm is utilized to perform aweighted fusion of the prediction results derived from the diverse base models. The resulting principalcomponents, which represent the most discriminative fused features, then form the refined input feature space fortraining the second-layer meta-model, thereby achieving a sophisticated and high-accuracy multi-level ensemblelearning framework. Experimental results show that the PCA-optimized Stacking model achieves a recognitionaccuracy of 97.19%, significantly outperforming traditional Stacking models and all individual models. Theexperimental results indicate that the recognition accuracy of the PCA-optimized Stacking model reaches97.19%, significantly outperforming both the traditional Stacking model and all individual models. Furthermore,the study provides valuable insights and a novel technical framework for lithology identification in sandstonetypeuranium mine borehole strata, contributing to more reliable and efficient geological interpretation in uraniumexploration and mining operations.