By extracting the Near Infrared (NIR) diffuse reflectance spectral characteristics from the post-harvest lotus seeds in different storage periods, the quantitative and qualitative analysis were applied to lotus seeds with the Soluble Solids Content (SSC) and dry matter content (DM) as criteria. The results of the Partial Least Squares Regression (PLSR) and distance discrimination (DA) models showed that the absorption spectra of lotus seeds and lotus kernels has clear relations to their SSC and DM. The PLSR models of SSC and DM of lotus seeds had the best performance in 5 941～12 480 cm-1 spectral region in this study. Their correlation coefficients of prediction were 0.74 and 0.82, and the correlation coefficients of calibration were 0.82 and 0.84, and the correlation coefficients of leave one out cross validation were 0.72 and 0.71. The PLSR model of SSC of lotus kernels was better in 7 891～9 310 cm-1 spectral region. Its correlation coefficient of prediction was 0.79, and the correlation coefficient of calibration was 0.84, and the correlation coefficient of leave one out cross validation was 0.77. The PLSR model of DM of lotus kernels is better in the full spectral region. Its correlation coefficient of prediction was 0.92, and the correlation coefficient of calibration was 0.89, and the correlation coefficient of leave one out cross validation was 0.82. For lotus seeds, the DA model in 5 400～7 885 cm-1 spectral region is the best with a correctness of 84.2%. And for lotus kernels, the DA model in 9 226～12 480 cm-1 spectral region is the best with a correctness of 90.8%. For dry lotus kernels, the discriminant accuracy of the DA model is 98.9% in the optimal spectral region. All kernels with membrane and plumule were correctly discriminated. This research shows that the NIR spectroscopy technique can be used to determine SSC and DM content of lotus seeds and lotus kernels, as well as to discriminate their freshness and also to discriminate dry lotus kernels of different age and the kernels with membrane and plumule.