The seed vigor was greatly affected by the storage condition. This work collected seeds that were affected by different storage conditions under real circumstance, and verified their germination rate difference by germination experiment. After that, we took some samples from them and measured the visible-near-infrared reflection spectra of each single grain. By adapting different spectral preprocessing techniques, combined with several machine learning modeling methods, we tried to distinguish the seeds according to their germination rates. In our experiments, some spectral pretreatment methods were compared, such as standard reflection spectrum correction. It also compared supervised machine learning modeling method such as support vector machine, K-near neighbor and discriminant analysis. From the perspective of recognition accuracy, we believed that standard reflection spectrum correction method can greatly improve the spectral difference of seeds with different survival rates, and thus achieve higher recognition accuracy through machine learning. At the same time, we compared the supervised machine learning modeling methods such as support vector, k near neighbor and distance discriminant analysis, and find that the standard reflection spectrum correction method combined with distance discriminant analysis can achieve hundred-percent accuracy of predicting different seeds category. Furthermore, in order to meet the requirements of rapid identification in practical applications, in the experiment we compressed high-resolution spectral data into low-resolution multi-channel band-pass spectral data, which can greatly reduce the spectral data length and save the time spent in training and classifying of various machine learner. The prediction accuracy of models trained by those simplified spectra data is still close to 90%. It fully demonstrates that the use of multi-channel broadband spectral data combining with the selection of appropriate machine learning modeling methods is sufficient to meet the general needs of the actual seed selection industry, and it is a potential technique for rapid identification of rice grain survival rates in the future. The experiment also used a variety of bandpass widths to simplify the spectra, and analyze and compare the effects of different bandpass widths on the recognition accuracy. In general, due to the increase in bandwidth, the length of data is reduced, and the recognition speed is faster, but the recognition accuracy is decreased. In the experiment, we changed the spectral bandwidth from 10 to 50 nm, and the recognition accuracy of the simplified spectrum after standard reflection correction decreased from 87.50% to 58.75%. In practical use, it is necessary to balance the recognition rate and the expected recognition accuracy, and select a reasonable bandwidth. This study verified that the simplified near-infrared reflectance spectroscopy can quickly and accurately identify the survival rate of rice seeds, which lays a foundation for rapid seed survival rate recognition technique based on bandpass filters in the future.