In order to realize the rapid identification of dopingsesame oil, the three-dimensional fluorescence spectra of the samples were measured by FS920 fluorescence spectrometer.The three-dimensional fluorescence spectrum was regarded as the gray scale graph,and the characteristic information of three-dimensional spectral grayscale was extracted directly by Zernike image moment without any pretreatment.Then, the characteristic information was clustered and analyzed by using the class mean method to identify the doping sesame oil and its constituent components. Finally, the generalized regression neural network (GRNN) was used to quantitatively analyze the components of the dopingsesame oil. Clustering analysis can identify adulterated sesame oil and its composition. The average relative error of the two groups was 2.23%, 8.00%, 9.70% and 9.70%, respectively. The results showed that the Zernike moments can effectively extract the characteristic information of the spectra. The proposed method of Zernike moments combined with clustering analysis and GRNN model can obtain satisfactory qualitative and quantitative analysis results, which will provide a new method for the identification of doping sesame oil.