To study the variation trend of major chemical composition of wheat seeds during short-time natural aging, the nondestructive technology based on near infrared spectroscopy (NIR) and support vector machines (SVM) is applied to evaluate the natural aging stage at the same time. There are 45 wheat samples collected in the experiment. The samples are scanned at the beginning and after natural aging for 4 months, 7 months and 9 months respectively by VERTEX 70 Fourier transform infrared spectrometer in large sample cup rotation sampling mode. The spectral standard deviations of each sample at four natural aging stages are calculated firstly. The standard deviations represent the statistical quantity of data dispersion. The obvious variation regions are screened according to the standard deviations calculated from the spectrums of 4 aging stages. To avoid abnormal discrete degree value caused by accidental factors, the averages of 45 samples spectrum discrete degree are calculated. The spectral peaks are mainly distributed in the area of 8 362, 6 950, 7 563, 5 319, 4 998 and 4 478 cm-1 according to the standard deviation. The region nearby 6 950 cm-1 reflects stretching vibration of O—H in liquid water, and the standard deviation value is greater. This illustrates the moisture changes remarkably during natural aging stage. The region nearby 5 319, 4 998 and 4 478 cm-1 reflect vibration information of primary amide, secondary amide and amide in protein. The standard deviation values at these peaks are all lower than the value of 6 950 cm-1, so the protein changes more slowly than moisture during aging stage. The region nearby 8 362 and 7 563cm-1 reflect secondary vibration information of C—H and the he standard deviation value is greater. There are C—H group in protein, starch, etc. of wheat seeds. It shows that comprehensive changes of protein, starch and other components are relatively strong. According to the above analysis, the multi-classification model has been built based on NIR and SVM to determine the 4 types natural aging stages. The sample set is divided into two parts randomly according to the ratio of 3∶1. The number of train sample is 135 and the number of test sample is 45. The best parameters of SVM are selected by grid searching. While the kernel function is RBF function, the penalty parameter is 8 and kernel parameter is 0.008 974 2, and the recognition rate of training set and test set reach to 99.26% and 99.78%. The results show that NIR technology combined with SVM can be applied to determine the natural aging stage of wheat seeds, which also provides a convenient and fast tool to monitor physiological characteristics changes during wheat seeds storage.