Existing edible oil detection technology cannot quickly and accurately identify edible oils sold in markets. Hence, in this paper, we propose a quick method of identifying edible oils. Fluorescence spectrum data of oil samples were obtained using the laser induction fluorescence(LIF) technique. Principal component analysis was used to extract characteristic information. Next, a multiclassification learning model was developed through the fusion algorithm of moth-flame optimization and kernel extreme learning machine (KELM) to identify the type of oil samples. Five types of oil samples were selected for experimental purposes, and 150 groups of fluorescence spectra were collected from each sample. Next, 600 samples were randomly selected to train the learning model, and the remaining 150 samples were used to test the trained model. Experimental results show that KELM model , extreme learning machine model and back propagation neural network model have similar average classification accuracy on the test set. However, the standard deviation of KELM model is less than those of other two models. This shows that KELM model has a stable classification performance and can quickly identify edible oils.