Aflatoxin is a highly toxic and carcinogenic substance with UV fluorescence characteristics. To study the detection of aflatoxins by hyperspectral imaging， aflatoxin was collected by a hyperspectral imaging system at 365 nm UV light. A total of 250 peanut grain samples in the 33 bands（400-720 nm） hyperspectral images. A method for predicting aflatoxin content based on the histogram quantization features of hyperspectral image decomposition abundance images was proposed. The method first obtained aflatoxin end-band spectra by N-FINDR endmember extraction， Spectral images were subjected to non-negative matrix factorization（NMF） to get aflatoxin abundance images. Based on this image， histogram quantization features were constructed. Partial least squares regression （PLS） and support vector machine regression （SVR） Vegetation abundance inversion， 50% cross-validation method obtained the average relative error of the two regression models respectively 29.95% and 12.16%， RMSE up to 0.0306. The results of this study have positive significance for the optical rapid detection of aflatoxin in agricultural products.