Rockburst is a difficult problem in underground resource development and infrastructure construction. With scientific and technological progress, more research technologies and methods to prevent rockburst have been proposed and applied. Research content has become more comprehensive, and positive progress and achievements have been made. However, due to the complexity of rockburst control factors and the sudden occurrence of rockburst, the occurrence mechanism and accurate prediction of occurrence intensity and time are still difficult worldwide problems. In this paper, the research development and achievements of rockburst are reviewed. Firstly, various definitions and types of rockburst are briefly summarized. Then, research progress and achievements in four aspects are comprehensively reviewed, including methods and techniques of rockburst research (theoretical research, numerical simulation, physical model tests and in situ monitoring), mechanism of rockburst, classification and prediction of rockburst (empirical criteria, in situ monitoring, mathematical model approaches and rockburst chart) and prevention and control of rockburst. Of particular significance is that the classification and prediction with prevention and control are summarized in detail. Finally, limitations, deficiencies and some promising directions for future research are listed.

Rockburst forecasting plays a crucial role in prevention and control of rockburst disaster. To improve the accuracy of rockburst prediction at the data structure and algorithm levels, the Yeo–Johnson transform, K-means SMOTE oversampling, and optimal rockburst feature dimension determination are used to optimize the data structure. At the algorithm optimization level, ensemble stacking rockburst prediction is performed based on the data structure optimization. First, to solve the problem of many outliers and data imbalance in the distribution of rockburst data, the Yeo–Johnson transform and k-means SMOTE algorithm are respectively used to solve the problems. Then, based on six original rockburst features, 21 new features are generated using the PolynomialFeatures function in Sklearn. Principal component analysis (PCA) dimensionality reduction is applied to eliminate the correlations between the 27 features. Thirteen types of machine learning algorithms are used to predict datasets that retain different numbers of features after dimensionality reduction to determine the optimal rockburst feature dimension. Finally, the 14-feature rockburst dataset is used as the input for integrated stacking. The results show that the ensemble stacking model based on Yeo–Johnson, K-means SMOTE, and optimal rockburst feature dimension determination can improve the accuracy of rockburst prediction by 0.1602–0.3636. Compared with the 13 single machine learning models without data preprocessing, this data structure optimization and algorithm optimization method effectively improves the accuracy of rockburst prediction.