In view of the defects of traditional test methods, such as large consumption of edible oil, cumbersome operation, and long time consumption, a new idea of fast nondestructive test of edible oil was put forward. In the experiment, five kinds of oil samples including mixed oil samples were selected. The laser induced fluorescence system built in the experiment was used to collect 500 groups of data, 400 groups of spectral data were randomly selected as the training set, and the remaining 100 groups of spectral data were used as the test set. After comparison, the stack autoencoder algorithm with better performance was selected to extract the features of the obtained fluorescence spectral data, and then the extreme learning machine was used for classification and recognition. Finally, the edible oil samples measured at different time were used to verify the generalization of model. The experimental results show that, under the recognition model constructed in this paper, the sample test network time is only 0.2 ms, and the classification accuracy can reach 100%. The sample test network used to validate the new sample can also achieve good classification effect, the classification is fast, and the accuracy is high. That is to say, the model established in this paper is reliable, and it can also realize fast nondestructive test of edible oil types while ensuring accurate identification.