为解决机器学习算法在样本较少时，所得岩爆烈度等级的预测结果存在可靠性不足的问题，采用一种基于交叉验证的XGBoost算法，并讨论其适用性。先选取岩石单轴抗压强度σc、单轴抗拉强度σt、洞室围岩最大切应力σθ、岩石弹性变形指数Wet以及岩体完整性系数KV等5个评价指标； 再以国内外岩爆实例数据为样本，通过多次交叉验证计算XGBoost算法岩爆预测准确率，与支持向量机算法、随机森林算法所得准确率比较； 最后对评价指标重要性进行分析。结果表明： 1）在样本较少时，样本划分和排序的随机性对预测结果影响较大，通过多次交叉验证求取预测结果平均值，可提高结果可靠性； 2）评价指标中KV与σθ重要性最大，σc重要性最小； 3）XGBoost算法具有较高的预测准确率，在岩爆烈度分级预测中具有一定适用性。

In order to solve the problem that when the samples are few,the reliability of the prediction results of rockburst intensity classification obtained by machine learning algorithms is insufficient, a XGBoost algorithm based on multiple cross validation is adopted and the applicability is discussed. Firstly, five factors including the rock uniaxial compressive strength σc, the uniaxial tensile strength σt, the maximum tangential stress of the surrounding cave σθ, the elastic deformation index Wet and the integrality coefficient of rock KV are selected as evaluation indexes. Then taking several rockburst instance data at home and abroad as samples, the rockburst prediction accuracy of XGBoost algorithm is calculated through multiple cross validation, and comparing with the accuracy obtained by support vector machine algorithm and random forest algorithm. Finally, the importance of evaluation indexes is analyzed. The results show that: (1) When with a small number of samples, the randomness of division and sorting for samples has great influence on prediction results, and the reliability of the results can be improved by calculating the average values of the prediction results through multiple cross validation. (2) Among the evaluation indexes, KV and σθ are the most important, while σc is the least important. (3) Due to high prediction accuracy, XGBoost algorithm has some applicability in the field of rockburst intensity classification prediction.