As the main ingredient of millet flour, the quality of starch determined the market price of millet flour. Gelatinization characteristic is one of the most important physical characteristics of millet flour, and the alkali spreading value is the main index that reflects the gelatinization characteristic directly. The differences in the alkali spreading the value of millet flour show the quality of amylose content. When the alkali spreading value becomes lower, on the contrary, the gelatinization temperature and amylose content become higher, eventually the lower the waxy of millet flour. This study employed the hyperspectral technique could with chemometrics methods to develop an approach for detecting the alkali spreading the value of millet flour, whose aim is to explore a rapid, nondestructive and low-cost method for predicting the alkali spreading the value of millet flour. First, the hyperspectral data of millet flour were collected, then the hyperspectral data matrix in the region of interest (ROI) in each pixel was computed. The results were meant in each wavelength of every sample. Then we used the rapid visco analyser (RVA) to measured the alkali spreading the value of millet flour. In the data processing, partial least square regression (PLSR) models were made after using competitive adaptive reweighted sampling(CARS) and random frog (RF) to extracted key wavelengths. The results showed that the highest predicted R_p was 0.77 in the PLSR of the full wavelengths, and that explained that the reflectance of millet flour could invert the alkali spreading the value of millet flour. The Rp in the other two methods were 0.72 and 0.7, and both were close to the previous result, these illustrated it was feasible to build the PLSR using CARS and RF. In order to improve the predicting accuracy, the full wavelengths were preprocessed by Savitzky-Golay (S-G), multiplicative scatter correction(MSC) and S-G+MSC. The performance of the PLSR model was better by using MSC predicted the full wavelengths (R_p=0.83). Then built the PLSR model again after extracting key wavelengths using CARS and RF, compared with the models without pretreatment, the R_p does not change much, which also shows that CARS and RF have a certain stability and can be used as reference methods for predicting the alkali spreading the value of the hyperspectral reflectance of millet flour. The results showed that the reflectance of millet flour could predict its alkali spreading value by using hyperspectral. This could supply a rapid, nondestructive and low-cost method of the alkali spreading value of millet flour, then provided the theoretical foundation for the rating, processing and alkali spreading value sensor of millet flour.