Mineral exploration is not only a critical component of new quality productive forces but also a driving force for their development.To address the challenges of sample imbalance caused by the limited number of known mineralization samples and the scarcity of mineralization information in gold mineralization prediction during mineral exploration,this study proposes the SMOTified-BRF model.This model applies the SMOTE method to augment the extremely limited known mineralization samples and employs the balanced random forest (BRF) method for prediction.Using the Hanbin-Xunyang area as the study area,geochemical data from stream sediments were analyzed using the SMOTified-BRF and BRF models and compared the gold mineralization prediction outcome and model performance.The results show that the SMOTified-BRF model achieves a higher AUC value (0.987 5) compared to the BRF model (0.972 6).Additionally,at the optimal threshold indicated by the Youden index,the predicted mineralized area ratio of the SMOTified-BRF model (1.95 %) is significantly smaller than that of the BRF model (12.23 %),demonstrating that the SMOTified-BRF model offers more accurate and efficient performance in predicting gold mineralization anomalies.