Ash content is a key index to measure the quality of coal and is one of the main indicators of coal product quality in coal mines and coal preparation plants.Aiming at the problems of low identification efficiency of traditional coal ash content and low quality of coal samples,a coal prediction model based on particle swarm optimization (PSO)and BP neural network is built and the purpose is to quickly identify the ash content in coal products and provide technical support for coal mining.180 standard pulverized coal samples are selected for the study,and the sample data of No.1～140 are used for the training set,and the sample data of No.141～180 are used as the test set.The PSO BP model is applied to study the coal ash characteristics,and the simulation results show that the optimized 6 dimensional BP neural network model,with the coefficient of determination R2 of 0.88501,is closer to 1,indicating that the established PSO BP model has better prediction performance,and the predicted value of ash is infinitely close to the true value of ash.In turn,it shows that the constructed gray prediction model has high prediction accuracy,which improves the generalization ability and prediction accuracy of the model,and provides some theoretical basis for the subsequent application of LIBS technique to coal detection.