With the growing global demand for industrial resources, the selection of efficient and reliable miningmethods has become increasingly critical. This study explores ways to enhance the scientific and accurate selection ofmining methods by integrating advanced machine learning techniques with an automated hyperparameter optimizationframework. Based on the collection and analysis of various mining conditions and techno⁃economic indicators, anoptimized random forest model based on Optuna was developed. The optimization process focused on tuning 4 keyhyperparameters of the RF model to improve its predictive accuracy and generalization performance. Experimentalresults showed that the optimized RF model evidently improved classification accuracy on both the training and testingdatasets. The Optuna-RF model significantly outperformed the unoptimized model. These results demonstrate the crucialrole of automated hyperparameter optimization in enhancing the generalization and predictive capabilities of machinelearning models and provide an effective technical approach for the intelligent selection of mining methods.