Infrared spectral imaging has been used in many fields, such as gas identification, environmental monitoring and target detection. In practical application, it is difficult to classify the spectrum between target and background due to cluster background and instrument noise. This article introduces the design of a modular FTIR imaging spectrometer based on interference optics and accurate control module. Based on this instrument, a spectral feature analysis and gas identification method is proposed and verified via experiment. The exact steps and algorithms include radiometric calibration, spectral pre-process, and spectral matching. First, multiple-points linear radiometric calibration is indicated to improve the calibration accuracy. Secondly, the spectral pre-processing methods are realized to decrease the noise and enhance the spectral difference between target and background. Thirdly, spectral matching based on similarity calculation is introduced to realize gas identification. Three methods, Euclidean distance (ED), spectral angle mapping (SAM) and spectral information divergence (SID), are derived. Finally, an experimental test is designed to verify the method proposed in this article, where SF6 is taken as the target. According to the results, various algorithms have different performance in time consumption and accuracy, and the proposed method is verified to be reliable and accurate in practical field test.