随着煤矿开采深度的增加，冲击地压频发，精准预测冲击地压危险等级对于煤矿安全开采至关重要，然而影响冲击地压的指标众多，且数据特性不同，针对单一算法无法满足不同指标要求的问题，提出了基于数据挖掘的指标信息融合模型预测方法。该模型由条件K近邻、加成随机森林和权重表决算法组成，首先将埋深指标输入条件K近邻算法，然后将其他指标输入加成随机森林算法，分别使用2种算法进行冲击地压危险等级预测，最后将2种算法的预测结果放入权重表决算法中进行表决，加权结果最大的危险等级即为最终危险等级。使用2010—2013年在千秋煤矿10个工作面采集的350条样本数据，进行数据挖掘与模型构建；使用2015—2016年在千秋煤矿4个工作面采集的20条样本数据进行实验验证。结果表明：基于数据挖掘的指标信息融合模型预测方法相比较于传统预测方法具有更高的预测准确性。

With the increase of coal mining depth, rock burst occurs frequently, and accurate prediction of rock burst hazard level is very important for coal mine safety mining. However, there are many indicators affecting rock burst, and the data characteristics are different. Aiming at the problem that a single algorithm cannot meet the requirements of different indicators, a prediction method of index information fusion model based on data mining was proposed. The model consists of conditional K nearest neighbor, a Bonus random forest and a weighted voting algorithm. Firstly, the buried depth index is input into the conditional K nearest neighbor algorithm. Then, other indicators were input into the random forest algorithm, and the two algorithms were used to predict the hazard level of rock burst. Finally, the prediction results of these two algorithms are put into the weighted voting algorithm for voting, and the risk level with the highest weighted result is the final danger level. In this paper, data mining and model construction were carried out using 350 samples collected from 10 working faces in Qianqiu Coal Mine from 2010 to 2013. The experimental verification was carried out using 20 sample data collected from 4 working faces in Qianqiu Coal Mine from 2015 to 2016. The results show that the prediction method in this paper has higher prediction accuracy than the traditional prediction method, which provides a certain reference for the early warning of rock burst hazard level in coal mines.