Accurately identifying vegetable oil species is significant in oil quality control, fraud detection, nutrition and health, and grain and oil trade. Accurate and rapid identification of vegetable oils is essential to ensure oil quality and maintain market supervision. This paper proposes a Principal component analysis-Ant colony optimization-Support vector machine (PCA-ACO-SVM) algorithm combined with Fourier transform infrared spectroscopy (FTIR) technology for rapidly identifying vegetable oil species. Six different kinds of vegetable oils were collected, and the absorption and transmission infrared spectra of the samples were measured by FTIR. PCA reduced the dimension of infrared spectral data, and the infrared spectral characteristics of oil products were extracted. The ACO algorithm optimizes the core parameters of the SVM classification algorithm. The optimized core parameters of the SVM classification model are C=1.1024043 and Gamma=0.1476193. This study uses the PCA-ACO-SVM algorithm to establish the identification model of vegetable oil species. The classification model was trained, and the parameters were optimized using the known types of oil products. The model was further applied to identify unknown oil products. By comparing with other algorithms, the accuracy and efficiency of the PCA-ACO-SVM algorithm in identifying vegetable oil types were verified. The results show that the PCA-ACO-SVM algorithm combined with FTIR technology can quickly and accurately identify the types of vegetable oil. This method has high classification accuracy and high computational efficiency in data processing, which is very suitable for the practical application of large-scale vegetable oil classification. In summary, the PCA-ACO-SVM algorithm proposed in this paper, combined with FTIR technology, provides a feasible solution for rapidly identifying vegetable oil species, which has high efficiency and accuracy. The scheme has broad application prospects in the food industry and quality supervision and is of great significance to the quality supervision of vegetable oil..