Sensitive terahertz bands related corn seed vigor were explored using terahertz time domain spectral reflection imaging technique combined with generalized two-dimensional correlation spectroscopy, and simultaneously the qualitative model to judge seed vigor nondestructively was established based on support vector machine and terahertz absorbance spectra. Take Zhongdi 77 (Corn variety) for example in this experiment. Firstly, there are 5 batches of different vigor seeds made by artificial aging (40 ℃, 100% relative humidity) for 0, 1, 2, 3, 4 days. 5 batches of seeds were conducted germination experiments according to GB/T 3543.4—1995 to obtain germination rate. Terahertz spectral images of seed samples were collected by Terapluse 4 000 terahertz time domain system with reflection imaging module. Because the composition of endosperm and embryo of corn seed is significantly different, it is meaningful to explore the correlation between the different tissues (endosperm and embryo) and the vigor in the aging process separately. A series of pretreatments was done to extract the terahertz absorbance spectra of different tissue of corn seed, such as denoising using double Gaussian filtering, image enhancement based on the peak-to-peak differential reconstruction and threshold segmentation. Take the aging days as the disturbance amount, and the generalized two-dimensional correlation spectrum analysis was carried out to the above-mentioned extracted spectra of endosperm and embryo. According to the preliminary analysis of the automatic peak and the cross peak in the synchronous and asynchronous spectra, the terahertz band closely related to seed vigor was mainly concentrated in the 75 and 36 cm-1 regions, and the spectral information at 75 and 36 cm-1 had great synergistic change and the change was consistent. Different aging days correspond to different vitality according to the germination experiment. Therefore, five-class support vector machines were built respectively based on endosperm and embryo absorbance spectra to identify seed vigor, and the recognition rate was only 59.34% and 71.28%. The results indicated that the model cannot precisely divide seed vigor to five levels. According to GB4401.1—2008, 85% germination rate was set as the threshold to divide seed vigor to two levels, then binary classifier based on support vector machines were built to distinguish seed vigor. The recognition accuracy of the endosperm and embryo test sets was 88.61% and 91.73% respectively. The recognition rate had improved significantly and the model can basically be used for rapid coarse screening of seed vigor. The experiment result showed terahertz reflection imaging with its rich fingerprint spectrum characteristics and lower energy is expected to be a new and powerful complementary technology to identify seed vigor rapidly and nondestructively.