Traditional coal origin identification method generally take the calorific value, volatiles, caking index, hardgrove index and crucible swelling number as the classification index, process complicated, use manpower and material resources and can’t get coal sample origin directly, take advantages of the near-infrared spectrum technology fast nondestructive testing, due to be collected in the original spectrum that contains some or false spectral data, using Leave-one-out cross validation based on SVM to eliminate abnormal sample of spectral data set, get the correct spectral information of coal sample spectra data sets, and construct the qualitative analysis model based on SVM algorithm and LVQ algorithm, complete based on near-infrared spectral analysis technology of coal origin identification, don’t need to make summary and coal samples of various indicators forecast. In view of the random parameter optimization problems in SVM model, the PSO-SVM model of loss parameters (C) and the radius of kernel function (g) are improved, get the optimal parameters, finally, calculation accuracy of the method above contrast model is introduced to evaluate and analysis. Experiments collect the near infrared spectrum of Canada, Russia, Australia, Indonesia and China’s five regions, all the data sets, a total of 305 samples, of which 10 simples is abnormal samples and the first 31 groups of the coal spectra were selected as training samples, 6 sets of data after as test samples. Results show that the classification accuracy of classification model can achieve 75% above, including the analysis of the SVM model based on PSO algorithm to improve the accuracy can reach 96.67%, only a sample appear problem, it will be realized quickly and efficiently based on near-infrared spectral analysis technology of coal origin identification.