Aiming at the problem that the current honey detection technology cannot identify the honey quickly and accurately， a method based on the organic combination of laser-induced fluorescence (LIF) and learning vector quantification (LVQ) to quickly identify honey is proposed. The author uses LIF to obtain the spectral data of honey， uses principal component analysis (PCA) to process the spectral data， and inputs the processed data into the established LVQ classification learning model for classification and recognition. The experiment selects 4 different types of honey， each collects 50 groups of honey spectrum data， 120 groups of honey spectrum data are randomly selected for the training of the LVQ neural network model for 4 types of honey， and the remaining 80 groups of honey data are input for training in the trained LVQ model. In this experiment， the time required for the LVQ classification learning model for honey classification and identification is 0.8s， and the accuracy of the LVQ classification learning model for honey classification and identification reaches 99.45%. The experimental results show that the organic combination of LIF and LVQ can meet the requirements of rapid and accurate identification of honey.