A new method was put forward to identify rice syrup adulteration in honey using three-dimensional fluorescence spectra technology combined with chemometric methods. The information of the three-dimensional fluorescence spectra was compressed and extracted using characteristic parameters method and the principal component analysis. Then, it can analyze honey adulteration combined with linear discriminant analysis (LDA) and the error back propagation neural network (BP-ANN) methods. The results showed that the prediction recognition rate of the LDA model was 94.44%, but that of the BP-ANN model can reach 100% when the number of principal components were four in the recognition experiment of adulterated honey, indicating that the BP-ANN model was more suitable for honey adulteration identification. This work demonstrated that the three-dimensional fluorescence spectroscopy combined with the BP-ANN model can be rapid, nondestructive, and accurate in identifying adulterated honey.