Aimed at the problems of too long modeling time and poor generalization in prediction modeling of mixed gas

principal component extraction（PCE）combined with Bayesian regularization neural network method is used.The mixed infrared absorption spectrum of four common polluted gases CH4

CO

SO2 and NO2 is analyzed

and each single gas volume fraction is obtained respectively.The network is constructed by programming with Matlab software

and the network parameters are optimized.The result shows that the modeling time of the network reduces from 4 250 s to 8 s

but the prediction goodness of fitting keeps mostly unchangeable

reaching to 95.1 %.Compared to conventional back-propagation（BP）neural network

the method has better prediction effect and practical significance in quantitative analysis of mixed gas in air pollution.