The present paper develops a new approach to the analyse of corn based on discrete Fourier transform (DFT). The experiment data is of 37 varieties of corn seed with the Fourier transform near infrared spectrometer in the wave number range from 4 000 to 12 000 cm-1. Analyse of the origin data found that as the wave number increases, the data noise also increases. Firstly, the paper defines a calculation method of interspecific and intraspecific differences Qm to measure the effectiveness of feature selection. Secondly, Qm was used to analyse the original data and DFT-section data. Experimental results show that by choosing data of DFT with wave number range from 4 000 to 7 085 cm-1, the mean value and the peak value of the the Qm curve markedly improved relative to the full band original data. The mean value was enhanced from the original 4.804 9 to 8.513 8, and the max of the peak value was enhanced from the original 35.924 0 to 60.821 6, while the min of the peak value was enhanced from the original 2.891 8 to 3.741 5. Data feature points (Qm value of large point) are more concentrated than the original data after DFT. Such a result is most conducive to extracting the characteristics of corn seed.