For quantitative analysis of multi-component gas mixtures, there are incomparable advantages for the quantitative analysis technology of characteristic spectrum. However, the efficiency and accuracy of quantitative detection depends on the capabilities of the spectral data processing algorithms. Optimizing the parameters of spectral analysis algorithms and improving the processing of spectral data are important means to improve the speed and accuracy of quantitative analysis. According to the problem in selecting parameter of support vector machine(SVM) when detecting quantitatively the concentration of multi-component gas in underground mine, an improved Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm was proposed. The algorithm is mainly used to research the problem that the multi-component gas mixed spectrum data is large and the spectral feature information overlaps. The algorithm constrains the convergence path of the PSO algorithm through particle variation, and it improves model optimization efficiency through particle information sharing, and it uses the setting of dynamic insensitive areas to improve model accuracy. A rapid quantitative detection system for multi-component gas was designed in underground mine. The infrared light source was drived by signal modulation module controlled by PC, and the signal light was irradiated on the detector by the air chamber with dust and steam filter. On the basis of the pressure and temperature sensor compensation, the detected optical signals were transmitted to the CPU by signal processing module. Finally, quantitative analysis of the gas concentrations for the various components was achieved by the improved PSO-SVM algorithm. On the basis of the actual sample gas collection and pretreatment in the underground, five kinds of gas components of CH4, C2H6, C3H8, SO2 and CO2 were tested. The concentration range of CH4 was 0～10%, and the concentration range of other gases was 0～10%. Infrared spectral data of these five gases were collected with Fourier infrared spectrometer. 800 groups of these gases were divided into 400 groups for calibration set and 400 groups for validation set. The quantitative analysis model of multi-component gas was established by SVM. The parameters of SVM were optimized by improved PSO, and the quantitative parameters were reconstructed by the obtained optimal parameters. The infrared spectral data collected by the algorithm and the traditional BP network algorithm were used to invert the gas concentration of each component. The experimental results show that the convergence range of the optimal value is reduced due to the constraint of the mutated particle, which improves the convergence speed. The modeling time of the algorithm is only 1/10 of that of the traditional method; Since the insensitive area is given by the spectral characteristics of the gas, the cross-sensitivity effect of the characteristic spectrum is reduced, which improves the prediction accuracy of the model. It improves the accuracy of model predictions, with an average error of about 1/5 of traditional methods. It is feasible to use improved PSO combined with SVM for quantitative analysis of multi-component gas in underground. The improved PSO-SVM algorithm has good applicability for the separation of multi-component gas mixed infrared spectral data, and it has certain practical application value.