针对传统燃爆机理研究方法存在耗时久、成本高、有效实验数据难以获取的问题，采用一种数据驱动与物理实验相结合的方法对半封闭空间置障条件下燃爆实验结果进行预测，并以燃爆实验数据为基础，开发了一种基于Adam优化算法下的BP神经网络预测模型，通过敏感性分析实现隐含层神经元个数的最优配置；以实验获得的火焰速度和最大燃爆压力作为特征样本数据进行训练和测试；采用R²(决定系数)评价指标来评估预测模型性能，并与RSM模型和岭回归模型进行对比。结果表明，采用Adam-BP模型预测火焰速度和最大燃爆压力相比RSM模型预测的R²分别提高了30%和16%，相比岭回归模型的R²值分别提高了10%和8%，并且Adam-BP模型鲁棒性相对较好。测试结果表明，Adam-BP模型在预混可燃气体燃爆实验结果预测中精度达到95%以上，因此，Adam-BP模型适用于预混可燃气体燃爆后果的预测，可为研究预混可燃气体燃爆后果提供一种快速预测方法。

Aiming at the problems of long time-consuming, high cost and difficulty in obtaining effective experimental data in traditional research methods of ignition and explosion mechanism, the experimental results of ignition and explosion in semi-enclosed space with barrier conditions were predicted by using a method combining data-driven with physical experiment. Based on the experimental data of combustion and explosion, a BP neural network prediction model in basis of Adam optimization algorithm was developed, and the optimal configuration of the number of neurons in the hidden layer was realized by sensitivity analysis. Taking the flame speed and maximum explosion pressure from the experiment as the characteristic sample data for training and testing, and the performance of the prediction model was evaluated by R² (determination coefficient) evaluation index, and compared with RSM model and ridge regression model. The results show that the flame velocity and the maximum detonation pressure predicted by Adam-BP model are 30% and 16% higher respectively than those predicted by RSM model, and is 10% and 8% higher than those of the ridge regression model with better robustness. The test results show that the accuracy of Adam-BP model is more than 95% in the prediction of the experimental results of pre-mixed combustible gases.Therefore, Adam-BP model is suitable for predicting the consequences of premixed combustible gas combustion and explosion, which can provide a fast prediction method for studying the combustion and explosion consequences of premixed combustible gas.