## 1 Introduction  
Rockburst, a geological hazard phenomenon that is a key concern in the fields of underground engineering and rock mechanics, refers to the brittle fracture and failure that occurs suddenly in the exposed rock mass in deep underground mining areas or regions with high tectonic stress. The underlying cause is the instantaneous, violent, and unreserved release of the strain energy accumulated within the exposed rock mass, which leads to a brittle fragmentation state of the rock mass similar to that caused by an explosive impact. The accompanying phenomenon of rockburst-induced pressure not only causes a large amount of rock to collapse and peel off, generating intense acoustic effects and impactful air waves that can damage the internal facilities of the mine, but the seismic waves generated can also propagate outward and pose a threat to the stability of surface buildings [1,2]. In view of this, how to effectively control rockburst has become a core problem in the fields of underground engineering and rock mechanics. At present, numerous scientific research efforts focus on uncovering the underlying physical mechanisms behind rockburst phenomena and, at the same time, strive to formulate forward-looking prediction strategies and practical prevention measures, aiming to anticipate the probability of rockburst occurrence in advance and minimize its negative impacts. At this stage, by leveraging advanced deep learning frameworks to construct a quantitative risk assessment model for rockburst, it provides technical support for accurately measuring the probability of such events [3]. In terms of rock mass monitoring techniques, acoustic emission monitoring and microseismic monitoring are two commonly used conventional methods in the industry [4]. The high-frequency part of the signal spectrum captured by AE monitoring is distributed in the range of \(10^{4}\) to \(10^{7}\) Hz [5]. With this characteristic, this technique has unique advantages in the study of the fracture process of rocks on a micro-scale [6] and has become a commonly used monitoring method in laboratory rockburst simulation tests. In contrast, the operating frequency range of MS monitoring is between \(10^{-1}\) and \(10^{-3}\) Hz [7]. Based on its high-sensitivity ability to capture the dynamic responses of large-scale rock masses, this technique is more often applied to the monitoring of rock mass stability at actual engineering sites. It is worth noting that, during the laboratory rockburst simulation tests relying on AE monitoring, researchers have found that high-amplitude AE signals are often difficult to detect on the eve of a rockburst [8]. At the same time, the fractal dimension index of AE signals shows a dynamic change characteristic of first rising steadily and then dropping sharply over time [9]. An in-depth analysis of AE spectrum evolution is crucial for evaluating and predicting rockburst risk. When it shows a low amplitude, wide band, and multi-peaks, the dominant frequency decreases [9] and the band shifts from a low to high frequency [10]. [PERSON] et al. 's team [11] found via studies that, when AE energy deviation rises and the daily high-energy sum peaks, rockburst is imminent. Also, sandstone AE location-monitoring shows increased high-energy AE events and inflection points in variance curves of AE rise time/amplitude and AF, and rise time and duration curves can serve as key precursors [12]. The AE signals in the rockburst process possess distinct TS characs. Currently, the industry usually uses advanced algorithm tools like GRNN [13], SVM [14], LW C4.5 Alg [15], RF, AdaBoost, GBDT, XGBoost, and LGBM [16] for the processing and analysis of such signals.