The study, targeting at 10 Maize varieties with different storage time and the same origin and harvest time, aims to study the effects of storage time on the results of the near infrared spectrum analysis technology applied in the near-infrared spectrum authenticity identification of maize single-kernel varieties. The authenticity model (monthly modeling) of breeds was established by using spectral data from January to identify the same samples which spectral data from February to December. The original spectrum was pre-processed by smoothing, first order difference and vector normalization. PLS-DA was used to establish the model for analysis and comparison, the results showed that the correct identification rate was decreasing month by month. The average correct identification rate of the model is reduced by 26.27% when the storage time is increasing from 1 month to 11 months, Which indicates that the longer the storage time of maize seeds is, the lower the accuracy of the near-infrared spectrum authenticity identification will be. This research also indicated that with the increase of the storage time of maize seeds, the spatial distribution of the spectral data of the same species but at different storage time is different. The discretization of spectral data becomes obvious, and the repeatability and consistency are reduced, which makes the accuracy of authenticity identification results of maize seeds is reduced. We endeavor to expand the models to centralize the range of the information that is easily interfered, that is, expand the spectral data collected under different environmental factors, instrumental factors and seed samples in different periods of time in 1 year to the modeling spectrum data to increase the inclusiveness of the prediction model of the near infrared spectrum based on the expanded data. Then, the inclusive model (joint modeling) has established by jointing the January and February modeling sets, after that, identifies the test set samples from March to December respectively, and then increases the model set spectrum data month by month, and the identifies the months that non-modeling set is located month by month. It taking JK968 as an example, the results showed that the accuracy of the model for the adjacent months of the modeling set is high, and then decreases month by month. When the feature spectrum of the model set is added from January to June, the average correct identification rate of the inclusive model can be more than 92%. In the above way, 10 maize varieties were tested, which can be seen that the correct identification rate of the inclusive model for maize seed authenticity is significantly higher than that of the single month model. The average correct identification rate of J92 and XY211 is increased by 11.58% and 7.71%, respectively. At the same time, in order to further improve the correct identification rate of the model, this study added the spectral data of the year 2016 to the modeling concentration of the inclusive model, so that the average accuracy identification rate of maize hybrids in 2017 reached 94.68%, and the inbred line reached 95.03%, providing the basis for further developing special models and practical equipment.