Sesame oil is rich in nutrients. Due to the high market price, adulteration is frequent, which seriously damages the interests of consumers and the healthy development of the market. Therefore, the development of a fast and accurate method for the identification of adulterated sesame oil (ASO) is of great significance for protecting consumer rights and the market health. To this end, this paper proposed a method for identifying ASO with wavelet moments (WMs) combined with three dimensional fluorescence spectra (3DFS). This method is simple and rapid, and can effectively identify ASO. In the article, Taking 43 samples (16 sesame oil, 9 kinds of rapeseed adulteration sesame oil, soybean adulteration sesame oil and corn adulteration sesame oil, respectively) as research objects. The main research contents and results are as follows: The 3DFS of the samples were obtained using a FS920 fluorescence spectrometer. Multiresolution signal decomposition (MRSD) was performed on the spectra using db2 wavelets, and then the 3DFS was reconstructed using the first-order discrete approximation coefficients of MRSD. The first two orders of WMs: W0,0, W1,0, W1,1, W0,1, W2,0, W2,1, W2,2, W1,2, W0,2, were separately used as feature to perform hierarchical clustering (HC) on the samples. Next, combined with Dunn’s cluster validity index (DVI), the classification quality and laws of the same-order and different-order WMs were studied, and the optimal WMs for identifying ASO were determined. Results: MRSD can remove noise and reduce the amount of spectral data by 72.4% on the basis of retaining the effective characteristics of the original spectra. To a certain extent, it can overcome the disadvantages of moment methods that large computational complexity and high-order moments are seriously affected by noise. Using one of W2,1, W2,2, W1,2, W0,2 to perform HC as a feature, the ASO can be easily and quickly identified. The same-order WMs (Wp,q) exhibit regularity as the p decreases q increases, and the effective WMs (EWMs) of the same order were determined. The target moments (TMs) W0,q≥2 and the optimal target moment W0,6 which can be used to identify ASO were determined. Simple and efficient identification of ASO can be achieved by computing HC with any determined WMs. This method can be extended to online measurements. The research ideas and conclusions provide a reference for the identification and classification of vegetable oils, and provide a means to protect consumer rights and market health.