Abstract: The accurate prediction of coal temperature plays a vital role in preventing and controlling the coal spontaneous combustion in coal mines.To predict the temperature of coal spontaneous combustion in a gob accurately and reliably, a long-term observation test of temperature and gases was implemented in the gob of 40106 fully mechanized top-coal caving face at Dafosi coal mine.A prediction model of coal spontaneous combustion in the gob based on random forest ( RF) method was proposed, and the prediction results were compared with the support vector machine ( SVM) and BP neural network ( BPNN) methods.The particle swarm optimization ( PSO) algorithm was employed to optimize the hyper-parameters of RF and SVM for establishing the PSO-RF and PSO-SVM prediction models with optimized parameters.The results indicate that RF, PSO-RF, SVM, and PSO-SVM models all had strong generalization androbustness.RF possessed a wide range of parameters adaptation in the modeling process. When the number of trees (n_tree) exceeded 100, the training errors tended to be stable, and the change of n_treehad no substantial impact on the prediction performance.Although the PSO algorithm could find the optimal hyper-parameters of RF, the RF model with the default parameters could obtain a satisfactory prediction performance.The prediction results of SVM were very sensitive to its hyper-parameters, PSO optimization could significantly improve its prediction accuracy, and its prediction performance depended on the optimal choice of hyper-parameters.The BPNN model exhibited excellent prediction results in the training stage, but it was prone to “over-fitting”, resulting in weak generalization and large errors in the testing stage.Through the application of coal spontaneous combustion prediction in other mines, the stability and universality of the RF method were verified, and good prediction performance could be obtained without complicated parameter settings and optimization, it could be further applied to other energy and fuel fields.