In this research, took the waxy corn seed “Jingkenuo 2000” as an example to research the identification of the the heat-damaged kernels of waxy corn seeds quickly and non-destructively and explore the effect of heat damage on waxy corn seeds. The multi-spectral imaging data of the control group and heat-damaged group were collected by Videometer near-ultraviolet-visible-short-wave near-infrared multi-spectral imager with embryo facing up and embryo facing down respectively. The single-point multi-spectral data from the embryo with embryo facing up, endosperm with embryo facing up and down were extracted respectively, while the multi-spectral data from the embryo and endosperm with embryo facing up were fused primarily. The standard deviations of all spectral variables were calculated after the baseline preprocessing to the multi-spectral data to analyse the effect of heat damage on waxy corm seeds according to the change of standard deviation of the data. Based on the multi-spectral data, the non-destructive identification models of the heat-damaged waxy corn seeds were developed by partial least square - discriminant analysis (PLS-DA) algorithm, which was compared with the models developed based on near-infrared spectra data. The result indicated that heat damage results in different effects on embryo and endosperm, however, the multi-spectral data and near-infrared data show the same trend of change. Based on the multi-spectral data, the identification models of the heat-damaged kernels of waxy corn seeds are developed. In the 3D scatter score plots of each model's first three principal components, the samples of the control group and the heat-damaged group show a certain separation trend. The accuracy of calibration data is between 96% and 100%, while the accuracy of cross-validation data is between 92% and 100%. The model developed by the fusion data of embryo and endosperm spectra with embryo facing up is of a higher accuracy, of which, the accuracy of calibration data is 100 %, and that of cross-validation data are between 98% and 100%. In contrast, the PLS-DA models of the heat-damaged waxy corn seeds are developed by near-infrared spectra data. In the 3D scatter score plots of the first three principal components of the models developed by the data of embryo facing up, embryo facing down, and the fusion of the two, the samples of the control group and the heat-damaged group show a good separation trend of which, the accuracy of the calibration and cross-validation are all 100 %. This research demonstrated that it is of good feasibility to identify the heat-damaged kernels of waxy corn seeds by near-ultraviolet-visible-short-wave near-infrared multi-spectral imaging technology rapidly and non-destructively. The standard deviation data of the multi-spectral variables are consistent with those of near-infrared spectral data. The calibration model by the fusion data of embryo and endosperm is of a higher accuracy for the multi-spectral data.