In the case of infrared spectroscopy for the monitoring of unknown gas components, it is necessary to qualitatively identify the gas components. LASSO variable selection technique based on multiple linear regression model is widely used in data analysis. In this paper, the LASSO method is introduced into the field of infrared spectroscopy, and a qualitative identification method based on LASSO variable selection technique combined with cyclic least squares (LCLS) analysis is proposed and verified by relevant experiments. The absorbance spectra of six components of CO, C-2 H-4 NH3, C-3 H-8 , C-4 H-10 , C-6 H-14 , and the absorbance spectra of a mixture of C-2 H-4 and NH3 were measured. The LASSO method was used to analyze the collected spectra with the spectra library build by our lab, and then the LCLS method was used to eliminate the interfering components. The experimental results show that LASSO combined with LCLS can effectively identify the target components in the spectrum, even in the spectral band with serious interference, most of the interference components can also be removed.