The research on classifying different kinds of vegetable oil is very important for food safety and quality supervision. The near-infrared spectroscopy could achieve a qualitative and quantitative analysis of samples with complex components. It has been widely applied to classifying different kinds of vegetable oil. The classification method based on two-dimensional correlation near-infrared spectroscopy is applied to recognize typical vegetable oil in this research. Two-dimensional correlation analysis was carried out with the concentration of n-hexane as the disturbance factor. Then the two-dimensional correlation analysis is calculated within the range of 6 001~6 063 cm^-1, in which the absorption features of different kinds of vegetable oils is obvious. And these feature information is reflected in a large number of data points (two-dimensional correlation synchronization spectrum matrix), so further extraction is needed to reduce the variable dimension. The diagonal elements of the two-dimensional synchro spectrum, that is, its autocorrelation spectrum, is always positive, representing the extent to which the spectral intensity varies with the disturbance factor at different wavelengths. Taking advantage of features extracting and data dimensions reduction, the principal component analysis is adapted to extract the feature of the autocorrelation spectrum. The Euclidean distance of principal components is calculated to classify different types of vegetable oil. The experimental results indicated that the proposed method is a benefit for automatic recognition and classification of typical vegetable oil. The PCA algorithm can effectively improve the recognition efficiency and robustness of the model. It provides a new concept for the analysis and processing of food quality sensing spectroscopy.