目前支持向量机（SVM）和随机森林（RF）等算法在煤矿巷道支护领域应用较少。研究了不同的机器学习模型进行支护参数设计的适用性，以建立一个更高性能的模型来实现锚杆支护的合理、科学设计。首先建立煤巷支护智能预测数据库：采用现场调研、问卷调查和文献检索等方式收集煤矿巷道样本；采用缺失值填补、箱形图修改离群点和局部异常因子剔除等方式对数据进行处理，建立煤巷支护数据库。提出一种基于合成少数类过采样（SMOTE）−遗传算法（GA）−SVM的煤巷支护参数预测模型：将数据库中的数据分成训练集与测试集，采用SMOTE技术平衡训练样本，提高模型对少数类样本的拟合能力；训练过程采用GA对SVM的超参数进行全局寻优，进一步提高模型整体性能。测试结果表明，SMOTE−GA−SVM模型的分类精度达到83.8%，比传统的SVM模型提高了21.8%。将SVM、人工神经网络（ANN）、RF、AdaBoost（ADA）和朴素贝叶斯分类器（NBC）等机器学习方法引入到煤巷锚杆支护参数预测中，建立对应的支护参数预测模型，比较结果表明：从最优到最差的预测模型排序分别为SMOTE−GA−SVM、RF、GA−ANN、SVM、NBC和ADA，6种模型的平均分类精度达69.9%，验证了机器学习方法在煤巷锚杆支护参数预测方面的可行性。在山西霍宝干河煤矿有限公司对SMOTE−GA−SVM模型进行了应用，模型预测准确率达87.5%，具有较强的适用性和可靠性。

Currently, algorithms such as support vector machine (SVM) and random forest (RF) are less applied in the field of coal mine roadway support. The paper studies the applicability of different machine learning models for support parameter design.Thus a higher performance model would be established to achieve reasonable and scientific design of anchor bolt support. Firstly, it is suggested to establish an intelligent prediction database for coal mine roadway support. Through on-site research, questionnaire survey, and literature search, the coal mine roadway samples are collected. The data is processed using methods such as filling in missing values, modifying outliers in box charts, and removing local abnormal factors to establish a coal roadway support database. The paper proposes a coal roadway support parameter prediction model based on synthetic minority oversampling technique (SMOTE) - genetic algorithm (GA) - SVM. The data in the database is divided into training and testing sets. The SMOTE technology is used to balance training samples, and improve the model's fitting capability for minority class samples. The training process uses GA to globally optimize the hyperparameters of SVM, further improving the overall performance of the model. The test results show that the classificaton precision of the SMOTE-GA-SVM model reaches 83.8%, which is 21.8% higher than the traditional SVM model. The machine learning methods such as SVM, artificial neural network (ANN), RF, AdaBoost (ADA), and naive Bayesian classifier (NBC) are introduced into the prediction of coal roadway anchor support parameters. The corresponding support parameter prediction models are established. The comparison results showed that the best to worst prediction models are ranked as SMOTE-GA-SVM, RF, GA-ANN, SVM, NBC, and ADA, with an average classificaton precision of 69.9%. The result verifies the feasibility of machine learning methods in predicting the parameters of coal roadway bolt support. The SMOTE-GA-SVM model is applied in Shanxi Huobaoganhe Coal Mine Co., Ltd., with a precision of 87.5% and strong applicability and reliability.