Abstract: Because of poor understanding about the mechanism of rockbust and about the effect factors, the statistic data of large amounts of rockburst are typical imbalanced data sets (IDS). On the basis of analyzing re-sampling technology, a novel hybrid re-sampling technique based on Automated Adaptive Selection of the Number of Nearest Neighbors (ADSNN-Hybrid RS) is proposed and applied to study the prediction of rockburst. This method takes advantage of both technology of improved Synthetic Minority Over-sampling Technique (SMOTE) method and Neighborhood Cleaning Rule (NCR) data cleaning method. In the procedure of over-sampling with the SMOTE method, blindfold new synthetic minority class examples by randomly interpolating pairs of closest neighbors were added into the minority class; and data sets with nominal features can not be dealt with. These two problems were solved by the automated adaptive selection of nearest neighbors and adjusting the neighbor selective strategy. As a consequence, the quality of the new samples can be well controlled. In the procedure of under-sampling, by using the improved under-sampling technique of neighborhood cleaning rule, borderline majority class examples and the noisy or redundant data were removed. The main motivation behind these methods is not only to balance the training data, but also to remove noisy examples lying on the wrong side of the decision border. The removal of noisy examples might aid in finding better-defined class clusters, therefore, allow the creation of simpler models with better generalization capabilities. In turn, it promises effective processing of IDS and a considerably enhanced classifier performance. The VCR rockburst data sets were employed as a sample IDS for classification and prediction. By adding extra artificial minority class samples as the expanded training set, experiment was conducted, which yields exactly consistent prediction results with the actual situation. The ADSNN-Hybrid RS and classification scheme we developed is feasible and reasonable for applications of IDS from engineering. Thus this method can be readily implemented to determine the controlling factors of engineering. Such a prediction can provide reasonable and sufficient guidance to design a safe construction scheme in deep mining engineering.