###### Abstract  
The prediction of rock bursts is of paramount importance in ensuring the safety of coal mine production. In order to enhance the precision of rock burst prediction, this paper utilizes a working face of the Gengcun Coal Mine as a case study. The paper employs a three-year microseismic monitoring data set from the working face and employs a sensitivity analysis to identify three monitoring indicators with a higher correlation with rock bursts: daily total energy, daily maximum energy, and daily frequency. Three subsets are created from the 10-day monitoring data: daily frequency, daily maximum energy, and daily total energy. The impact risk score of the next day is assessed as the sample label by the expert assessment system. Sample input and sample label define the data set. The long short-term memory (LSTM) neural network is employed to extract the features of time series. The Bayesian optimization algorithm is introduced to optimize the model, and the Bayesian optimization-long short-term memory (BO-LSTM) combination model is established. The prediction effect of the BO-LSTM model is compared with that of the gated recurrent unit (GRU) and the convolutional neural network (1 DCNN). The results demonstrate that the BO-LSTM combined model has a practical application value because the four evaluation indexes of the model are mean absolute error (MAE), mean absolute percentage error (MAPE), variance accounted for (VAF), and mean squared error (MSE) of 0.026272, 0.226405, 0.870296, and 0.001102, respectively. These values are better than those of the other two single models. The rock explosion prediction model can make use of the research findings as a guide. rock burst; data monitoring; microseismic energy; BO-LSTM; neural network 2  
and real-time monitoring. The most significant advantage of microseismic monitoring technology is its capacity for real-time and dynamic monitoring of the dynamic disaster process in mines [10]. A significant number of scholars from both domestic and international academic institutions have conducted extensive research on the prediction of rock bursts based on microseismic energy changes. [PERSON] et al. [11] proposed a quantitative-trend early warning method for rock burst risk based on the maximum daily microseismic energy and the total number of microseismic energy/frequency deviations, analyzing the microseismic precursor information law of rock bursts. [PERSON] [12] used microseismic monitoring system to collect microseismic signals before and after the occurrence of rock bursts, analyzed the time series characteristics of microseismic signals, and used the FFT method and fractal geometry principles to study the spectrum characteristics and distribution variation in microseismic signals. It can be observed from the aforementioned research that the prevailing methodologies for monitoring rock bursts through microseismic energy frequently employ empirical analogy or mathematical statistics to identify early warning indicators and discriminant criteria, thereby enabling the prediction of the risk of rock bursts [13; 14]. Nevertheless, the general mathematical statistics method is inadequate for mining the extensive monitoring data set, and the prediction accuracy of rock burst risk necessitates further improvement. Deep learning represents a significant branch of artificial intelligence [15]. Its effectiveness is contingent upon the availability of a substantial quantity of data, which is then employed to train the model [16]. Deep learning exhibits a robust capacity for adaptive feature learning, rendering it an optimal choice for the analysis of rock burst monitoring data within the context of big data. [PERSON] et al. [17] developed an intelligent prediction model for coal and gas outbursts based on data mining, and validated the efficacy of the system using microseismic monitoring data from the heading face. The results indicate that the early warning level is largely consistent with the actual situation.