岩爆是深部资源开采过程中亟待解决的问题之一。为安全高效地预测岩爆灾害，提出一种基于链式方程多重插补法(MICE)与卷积神经网络(CNN)的岩爆烈度等级预测模型(MICE-CNN)。基于岩爆的主要影响因素与获取条件，建立预测指标体系，搜集120组岩爆案例原始数据，运用拉依达准则进行异常值处理，应用MICE选取RF、BLR、ET、KNN 这4种插补模型进行缺失值插补，结合Mean、Median 这2种传统插补方法，依据ERMS选取最优模型进行数据插补得到完整数据集，将数据平铺为6×1×1的一维图像数据作为输入层，结合输入层大小进行计算，确定卷积核与池化核大小均为3×1，特征边缘处理方法为前后补零。添加批量归一化层增加模型稳定性与收敛速度，优选ReLU激活函数、SGDM优化器函数。对CNN预测模型进行训练，训练集与验证集的准确率分别为100.00%、91.67%。建立RBF、SVM与PNN模型，分别对3个模型与CNN模型输入测试集数据进行对比验证，CNN模型预测结果准确率高于其他模型，为91.67%；对比PNN模型与CNN模型的混淆矩阵，CNN模型误判结果比实际结果岩爆程度高，即误判后的安全性更好，表明MICE-CNN岩爆等级预测模型切实可行。

Rockburst is one of the urgent problems to be addressed in the process of deep resource extraction. In order to predict the rockburst disasters safely and efficiently, a rockburst intensity grade prediction model (MICE-CNN) based on the Multiple Imputation by Chained Equations (MICE) and Convolutional Neural Network (CNN) was proposed. Specifically, a predictive indicator system was established based on the main influencing factors and the acquisition conditions of rockburst. A total of 120 sets of raw data from rockburst cases were collected, with the outliers processed by pauta criterion. Then, the missing data were interpolated with the four interpolation models of RF, BLR, ET and KNN, which were selected using MICE. Besides, data interpolation was performed with the optimal model selected according to ERMS, in combination with the two traditional interpolation methods (Mean and Median), resulting in a complete data set. In addition, the data were flattened into a 6×1×1 one-dimensional image data as the input layer, and the sizes of the convolutional kernel and pooling kernel were calculated to be 3×1 based on the size of the input layer. Moreover, zero-padding was applied for the feature edge processing. Batch normalization layers were added to improve the model stability and convergence speed. Thus, ReLU activation function and SGDM optimizer function were selected. Further, the CNN prediction model was trained, with accuracy rates of 100.00% for the training set and 91.67% for the validation set. Meanwhile, the RBF, SVM and PNN models were established for the comparison and verification of their test set data with that of the CNN model. Generally, the CNN model shows higher accuracy (91.67%) than the other models. By comparing the confusion matrix of the PNN model with the CNN model, it is found that the CNN model tends to overestimate the degree of rockburst compared to the actual results, indicating better safety after misjudgment. This demonstrates the feasibility of the MICE-CNN prediction model of rockburst intensity grade.