At ambient temperature and atmospheric pressure, making use of a photoconductive-antenna terahertz time-domain spectrograph and a self-designed air chamber, the terahertz time-domain spectroscopy (THz-TDS) technique test of methanol gas in the range of 0.1~3.0 THz shows that the methanol gas has no obvious absorption peaks in the range of 0.1~3.0 THz and has obvious absorption peaks in the range of 0.1~1.0 THz. In order to improve the determination accuracy of the concentration of the methanol gas, the author detected 15 groups of methanol gas with different concentrations on the basis of the relationship between the strengths of 15 characteristic absorption peaks of different locations and the concentration of the methanol gas, and obtained the difference curve of the of the characteristic absorption peaks. Based on the function approximation of BP neural network, the author optimized the initial weights and biases of the BP neural network by using the GA the genetic algorithm, which has higher rate of convergence to prevent from getting into local optimum easily, and constructed the mathematical model with the purpose of predicting the methanol gas concentration. The test results show that the neural network is applicable to predict methanol gas in the volume concentration range of 0.028 3~0.424 6 m3·L-1, the average relative standard deviation of the 2 sets of samples is 1.7%, the average recovery rate is 98%, the error precision of the neural network is 10-1, and correlation coefficient of the measured values and the predicted values is 0.996 77. The test basically achieved ideal predicted results. The research results obtained experimental data of methanol gas in the terahertz frequency band and found that the method of combining terahertz time-domain spectroscopy with GA-BP neural network can effectively detect the volume concentration of methanol gas, and provided a new method for the detection of concentration of methanol gas.