Abstract: A quantitative analysis model of mine gas concentration based on improved support vector machine (SVM) was adopted. Five mine gases were used for experiment, which included methane, ethane, propane, n-butane and carbon dioxide. Mid-infrared spectral data of these five gases and mixed gases were collected with Fourier infrared spectrometer. 236 groups of these mixed gases were divided into 186 groups for calibration set and 50 groups for validation set. Principal component analysis (PCA) was used to reduce the dimensionality of the infrared spectral data, and 3 eigenvalues were extracted as input, which helped to improve convergence speed and reduce calculation time. Particle swarm optimization (PSO) and genetic algorithm (GA) were used to optimize parameters of support vector machine (SVM) method respectively, and PSO was adopted for its better optimization effect over GA. The mixed gases were detected through this algorithm, and experiment results show that the average errors of concentration predictions of five gases are all less than 1.78%, and the maximum errors of concentration predictions of five gases are all less than 4.98%. The time cost for concentration prediction is all less than 103 s for the 50 groups. This suggested that the improved SVM method based on PSO can be used to predict the gas concentration accurately, and can meet the requirement of real-time detection of mine gases, which has great value in the study of concentration prediction of mine gases.