###### Abstract
[19] summarized the law of mine pressure behavior in different mining stages and analyzed the differences in mine pressure behavior in fully mechanized mining faces with different mining heights. The above research adopted the traditional methods of evaluation analysis, numerical simulation, and field measurement to analyze the rules and characteristics of the occurrence of mine pressure. These articles provide theoretical guidance for the research of this paper. However, most of these studies were carried out after mine pressure appeared, and the aim of early prediction is not achieved. Now, more researchers are using data-driven methods and machine learning techniques to predict mine pressure in advance. [PERSON] et al. [20] established a pressure prediction model of MBCT-SR-RF based on random forest and showed that the MBCT-SR-RF prediction model had higher prediction accuracy than BP neural network and SVM. [PERSON] [21] established a prediction model of mine pressure behavior in the working face based on machine learning through similar simulation experiments. [PERSON] [22] conducted a regional analysis and prediction on the pressure law of the working face roof based on the big data of the working resistance of the working face support. [PERSON] et al. [23] proposed a prediction method of mine pressure in a fully mechanized mining face based on a manifold regularization domain adaptive function link prediction error integration algorithm. [PERSON] et al. [24] established a prediction model of mine pressure data based on a back-propagation neural network. [PERSON] et al. [25] constructed a two-period dynamic analysis and prediction model of mine pressure via means of field measurement, data mining, and theoretical analysis. [PERSON] [26] established a prediction model of working face roof mine pressure based on the optimized GA-BP neural network. The training results showed that the prediction effect of the optimized GA-BP neural network model was better than that of the BP neural network. The above studies achieved significant results in the prediction of mine pressure numerical value and mine pressure behavior based on machine learning methods, which provide valuable research experience for the establishment of a mine pressure hazard prediction model in this paper. However, they mostly used regression algorithms and will eventually obtain continuous values, not a category. Managers often need to quickly and accurately predict whether the mine pressure data are abnormal or dangerous. When this occurs, a classification algorithm will play an indispensable role. The research purpose of this paper is to predict whether danger exists in coal mine pressure, help managers predict and judge the danger caused by mine pressure exceeding the threshold, and make appropriate decisions in due time. In order to manage users more conveniently, the output results only need to be the presence of danger and the [PERSON] of danger, so as to provide a decision-making basis for managers. It is clear that this problem is a binary classification problem. [PERSON] et al. [27] analyzed the differences between traditional prediction and machine learning early warning and listed evaluation indicators for evaluating rock burst, but they did not put forward specific solutions for a certain problem. [PERSON] [28] used the Gaojiapu Coal Mine as the research object and proposed a rock burst prediction method based on the deviation value of mine earthquake precursor characteristics and the dynamic change rate as the index. This study only predicted the impact of one working face in a single mining area. The limitations of the study are extensive. [PERSON] et al. [29] put forward \"global-region-local\" multi-parameter comprehensive index rock burst monitoring technology for the Huo tai Mine. This technology can improve accuracy through the real-time monitoring of stratified and complementary regions. However, research has focused more on making predictions from different ranges to improve accuracy. [PERSON] et al.