Sorghum-Sudan Grass is rich in crude protein and carbohydrate, suitable for silage treatment. High-quality seeds are a prerequisite for animal husbandry development, and germination rate is one of the most conventional indicators to test the seed quality. Therefore, testing and screening the germination rate of the seeds prior to sowing is essential. The germination test method is currently used to detect seed germination rate, which has a long cycle and high cost. In this study, a rapid and non-destructive method based on NIR was proposed to detect the germination rate of sorghum-sudangrass seeds. The near-infrared diffuse reflectance spectra of the seed samples were collected with 1-Der and 2-Der processing. Moreover, comparative analysis of the parameter values obtained for $R^{2}_{c},R^{2}_{p}$, RESEC and RMSEP was also performed. The support vector machine (SVM) was used for modeling, and the LIBSVM software package in Matlab was used to realize the SVM training and detection process to detect the seeds of sorghum-sudangrass seed with different germination rates. Using the Unity scientific 2600 XT Near-infrared spectrometer, 100 groups of sorghum-sudangrass seeds from different provinces were selected as samples. Before the experiment, the broken seeds and seeds that did not germinate were removed, and the germination test was carried out in the incubator. The germination rate of 100 samples was obtained, and the germination rate ranged from 41% to 64%. The seed samples were spectroscopically scanned and were randomly divided into calibration set (70 samples) and test set (30 samples). In this paper, the 1-Der and 2-Der method was used to preprocess the spectrum of sorghum-sudangrass seeds. SVM modeled the preprocessed data, and its parameters were analyzed. The results showed that the correlation coefficients of the training set ($R^{2}_{c}$) and test set ($R^{2}_{p}$) were 0.94 and 0.92 respectively, and the root mean square error of correction (RMSEC) and root mean square error of prediction (RMSEP) were 0.21 and 0.25 respectively, which reflected that the model was the best when the 1-Der was used to preprocess the seed data. When c=2 896.309 4, g=0.5, the detection accuracy of the test set was 96.666 7% (29/30) by using Rbf core functions of SVM modeling. These results suggested that the model was feasible to predict the seed germination rate, and could be used as one of the rapid and non-destructive detection methods for the preliminary detection of seed germination rate of sorghum-sudangrass could effectively promote the seed production.