## 1 Introduction  
Rockburst is one of the typical dynamic hazards in coal mining. It is a phenomenon of rock bursting and ejection caused by a sudden and violent release of the potential energy accumulated in the rock body under certain conditions, seriously affecting the safe production of the coal industry [1]. Many factors affect rockburst occurrence, and each factor is characterized by complexity and random uncertainty. Scholars worldwide have proposed relevant theories, such as stiffness theory, strength theory, energy theory, and three\(-\)factor theory [2, 3], which have formed the theoretical basis of coal mine rockburst research, and have laid a research foundation for the monitoring and early warning of rockburst. The widely used monitoring and early warning techniques of rockburst [4, 5, 6, 7] based on the physical and mechanical properties of the coal rock body mainly include the borehole coal dust method and the mine pressure observation method. From the geophysical perspective, there are such methods as the microseismic monitoring method, the electromagnetic monitoring method, and the ground sound monitoring method, but due to the difficulty in fully reflecting the complex phenomena in the breeding process of rockburst by single\(-\)parameter monitoring and early warning, there is still no unified standard for rockburst monitoring and early warning, making it more difficult to predict the risk level of rockburst accurately. On this basis, scholars have comprehensively analyzed various monitoring data from experience\(-\)driven and mechanism\(-\)driven perspectives. They have proposed a variety of integrated multiparameter safety evaluation and prediction methods for the burst risk level, in which the integrated index method, the possibility index method, the fuzzy C\(-\)mean clustering algorithm, the entropy\(-\)based hierarchical analysis [8; 9; 10; 11], and other mathematical methods are used. However, the index weight determination and calculation depend on the evaluator's expertise and the authenticity and integrity of the information in the evaluation area. Additionally, with difficulty in making a practical joint analysis of monitoring results, the desired monitoring effect is not achieved. Many experts and scholars have tried to use a data\(-\)driven approach to predict rockburst in recent years. They have achieved some results using machine learning techniques. [PERSON] [12] used an improved fruit fly optimization algorithm to select the input layer weights and the implied layer thresholds of the extreme learning machine (ELM) to construct a rockburst hazard prediction model. [PERSON] [13] optimized the kernel parameter \(\sigma\) and penalty factor \(t\) of the least\(-\)squares supported vector machine (LSSVM) by the particle swarm algorithm to construct a rockburst hazard classification prediction model. He used it to achieve good results in practice. [PERSON] [14] predicted rockburst classification using a combination of numerical simulation and neural network methods, which is essential for the roadway's early and later construction safety. [PERSON] et al. [15] divided rockburst prediction and warning methods into long-term prediction and short\(-\)term warning by analyzing the relevant literature at home and abroad. They outlined the progress and problems in applying the two methods. [PERSON] [16] used a DNN neural network combined with an SVM classifier to achieve a long-term rockburst prediction. [PERSON] [17] established a CNN\(-\)LSTM model to predict the future state of the rockburst characteristic variables and used the PSO\(-\)GRNN model according to the future state to predict the \(t+1\) rockburst level. Most of the above-mentioned prediction models and methods of the rockburst risk level are for the hazard warning of the current moment. The universal applicability of long-term prediction methods, however, requires further study. Based on the above research, this paper takes a data-driven perspective.