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This study presents a novel approach to rockburst prediction in deep mining operations using microseismic (MS) monitoring data and advanced ensemble learning techniques. The research addresses the critical issue of class imbalance in MS event identification by integrating the SMOTE-ENN algorithm, achieving significant improvements in automated processing efficiency and predictive accuracy.