The Fourier transform infrared spectra absorption peaks of alkane gases are overlapping seriously in the mid-infrared region. A wavelength selection method based on the impact value of variables and population analysis (IVPA) is proposed to select the wavelength of five alkane gases infrared spectra composed with methane, eth, propane, iso-butane and n-butane. IVPA algorithm will go through a number of iterations to select variables. In each iteration, the variables are divided into sample space and variable space. The impact value of variables is calculated in the sample space. According to the impact value, the variables are divided into elite variables and normal variables by using the weighted bootstrap sampling technology. Meanwhile, in the variable space, the frequency of each variable in the optimal model is counted. Finally, the variables with a lower frequency of normal variables are eliminated by the exponential decay function, and the root means squared error (RMSE) value obtained during each iteration is recorded. The variable subset corresponding to the minimum RMSE as the final selected variable. The proposed algorithm is tested by alkane dataset, and the results are compared with stability competitive adaptive reweighted sampling (SCARS) and iteratively variable subset optimization (IVSO) variable selection method proposed in recent years. Taking iso-butane analysis results as an example, the minimum cross-sensitivity of IVSO, IVPA and IVPA to the other four gases was 0.67%, 0.56% and 0.11%, respectively. The maximum cross sensitivity was 1.69%, 1.49% and 1.02%, respectively. The relative errors of iso-butane prediction were 1.94%, 1.65% and 0.51%, respectively. The number of selected variables by the above three methods is 52, 17 and 13, respectively. The results show that the IVPA method selected the least variables, only 0.36% of the original spectral data, obtained the lowest cross sensitivity for the other four gases, and got the most accurate prediction for iso-butane, which shows that the proposed wavelength selection method can be applied to the absorption overlapping spectra, and can improve the prediction accuracy and efficiency of the analytical model.