## 2 Literature Review  
With the increasing scale of mining and underground engineering, especially the rapid development of deep underground mining, the problem of predicting rockburst hazards has become increasingly complex and challenging. To address this challenge, many scholars have begun to extensively explore and utilize advanced machine learning techniques to predict the occurrence of rockbursts, thus promoting a large number of data-driven research efforts [4]. Among the many approaches, machine learning and deep learning models have been widely used in rockburst prediction due to their superior data modeling capabilities and good predictive performance. [PERSON] et al. [5] proposed a prediction framework that combines a random forest-based metric weight optimization method (RF-CRITIC) with an improved cloud model, which is able to effectively perform the prediction of short-term rockbursts. The method significantly improves the reliability and accuracy of prediction through multiple feature selection and fusion. [PERSON] et al. [6], on the other hand, proposed a random forest (Op-RF) model based on Optuna optimization, which significantly improves the prediction performance of the model through efficient hyperparameter optimization. Their validation results showed that the method achieved an area-under-the-curve (AUC) score of 0.984 in rockburst prediction, demonstrating a strong classification capability. In contrast, [PERSON] et al. [7] proposed the DeepForest model, which takes into consideration the contribution of each input variable to the occurrence of rockbursts by means of a multilevel integrated learning mechanism and sensitivity analysis. The authors demonstrated the effectiveness of the model in dealing with fewer input parameters, especially for rockburst prediction in deep mines. In addition, [PERSON] et al. [8] further extended the application of deep learning in rockburst prediction by employing a deep learning algorithm with a complex network structure and verified the great potential of deep learning models in rockburst prediction. In rockburst grade prediction, the selection of appropriate evaluation indicators plays a crucial role in the effectiveness of the model. If there are too few discriminating indicators, the key factors affecting rockbursts may not be fully reflected; however, if too many indicators are selected, the difficulty of data collection and processing is substantially increased [9]. Moreover, too many indicators may cause redundancy of information instead of affecting the prediction effect of the model. Therefore, how to balance the quantity and quality of indicators is always an important challenge in research. Currently, researchers have different approaches to choosing evaluation indicators for rockburst prediction. For example, [PERSON] et al. [10] directly selected six indexes as input variables when building the particle swarm optimized extreme learning machine model (PSO-ELM), namely, the maximum tangential stress of the surrounding rock (\(\sigma_{\theta}\)), the uniaxial compressive strength of the rock (\(\sigma_{c}\)), the tensile strength (\(\sigma_{i}\)), the stress ratio (\(\sigma_{\theta}/\sigma_{c}\)), the brittleness ratio of the rock (\(\sigma_{\theta}/\sigma_{i}\)), and the elastic energy index (\(W_{\text{et}}\)). [PERSON] et al. [11] directly chose four indexes, namely, maximum tangential stress, elastic energy index, uniaxial compressive strength, and uniaxial tensile stress. Meanwhile, [PERSON] et al. [7] used seven indicators in their deep forest model, namely, maximum tangential stress, uniaxial compressive strength, tensile strength,elastic strain energy index, stress concentration factor, and rock brittleness indices B\({}_{1}\) and B\({}_{2}\) of the surrounding rock, which significantly expanded the evaluation dimensions. On the other hand, the method proposed by [PERSON] et al.