岩爆烈度等级的准确预测对减轻乃至消除岩爆危害具有重要意义.针对岩爆烈度等级预测模型特征选取模糊和预测准确度不高问题，提出了一种ReliefF-Pearson特征选择下基于SSA-SVM-AdaBoost算法的岩爆等级预测模型.结合ReliefF的权值思想和Pearson系数的相关性原理对特征指标进行选择，利用麻雀搜索算法（SSA）优化支持向量机（SVM）以获得最优模型初始参数，将多个SSA优化后的SVM作为弱分类器组成自适应增强学习算法（AdaBoost）的强分类器.首先通过收集分析国内外岩爆案例数据，选取7种特征指标构成原始特征空间，然后利用ReliefF-Pearson从原始特征空间中筛选出4维优势特征，采用随机过采样对数据进行处理，最后将其输入到SSA-SVM-AdaBoost模型中进行分类预测.研究结果表明：基于ReliefF-Pearson的特征选择方法能够有效提取优势特征；基于多SSA-SVM的AdaBoost模型预测准确率相较于SSA-SVM和单层决策树AdaBoost模型均提高12.5%，相较于SVM提高31.25%，说明SSA-SVM作为弱分类器在分类性能上要优于单层决策树，AdaBoost增强算法集成多个单分类器要优于单个分类模型，且数据过采样处理没有影响模型预测集准确率，表明SSA-SVM-AdaBoost模型可有效应用于岩爆烈度等级预测，为岩爆预测问题提供新思路.

Accurate prediction of rockburst intensity grade is of great significance for mitigating and eliminating rockburst hazards. Aiming at the problems of uncertain feature selection and low prediction accuracy of rockburst intensity grade prediction model, in this paper it proposes a rockburst grade prediction model based on SSA-SVM-AdaBoost algorithm with ReliefF-Pearson feature selection. The method combines the weight idea of ReliefF and the correlation principle of Pearson coefficient to select feature indexes, and SSA-SVM-AdaBoost algorithm is proposed by using the sparrow search algorithm （SSA） optimized support vector machine（SVM）classifier as the AdaBoost weak classifier to solve the multiclassification problem. First, 7 kinds of feature indicators are selected to form the original feature space by analyzing rockburst case data, then the 4-dimension advantage features are selected by ReliefF-Pearson method. The data is processed with random oversampling before input SSA-SVM-AdaBoost prediction model. The research results show that the feature selection method based on ReliefF-Pearson can effectively extract advantage feature indicators. Compared with SSA-SVM and AdaBoost based on single-layer decision tree, the prediction accuracy of SSA-SVM-AdaBoost model is improved by 12.5%, and 31.25% compared with SVM. It shows that SSA-SVM as a weak classifier is better than a single-layer decision tree in classification performance, and the AdaBoost enhancement algorithm integrating multiple single classifiers is better than a single classification model. Data oversampling process does not affect the accuracy of the model prediction set, but improves the prediction accuracy of the training set. It is proved that the proposed model can be effectively applied to rockburst intensity grade prediction, which provides a new perspective for this problem.