针对瓦斯超限预测中存在的精度不足、泛化性能弱以及预警结果不准确等问题，提出了一种基于粒子群算法（particle swarm optimization, PSO）来对反向传播神经网络（back propagation neural network, BP）以及支持向量机（support vector machine, SVM）进行优化，该方法首先利用PSO对BP神经网络的参数进行寻优，以提升神经网络的预测精度，基于BP神经网络的预测结果，利用PSO优化后的SVM进行瓦斯浓度超限的预警，对不同等级的瓦斯超限风险进行预测和分类。结果显示：PSO-BP预测模型的均方根误差（root mean square error, RMSE）、平均绝对误差（mean absolute error, MAE）和决定系数（coefficient of determination，R²）指标反映良好，优化后的神经网络预测精度得到显著提升，SVM模型的预警准确率较高，且具备良好的泛化能力，可以满足煤矿开采过程中瓦斯浓度超限控制的实际应用需求。

PSO(particle swarm optimization), BP (back propagation neural network), and SVM (support vector machine) were combined to improve the accuracy of prediction and early warning. According to the prediction results of PSO-BP, the SVMs optimized by particle swarm optimization algorithm were used to give early warning of gas concentration exceeding the standard, and the risk of gas exceeding the standard was predicted and classified to different degrees. The results show that the RMSE, MAE and R² indexes of the PSO-BP prediction model reflect well, the BP prediction effect is significantly improved after PSO optimization, the early warning accuracy of the SVM model is high, and the generalization ability is strong, which meets the practical application needs of gas concentration overrun control in the coal mining process.