Edible oil is a necessity in daily life. The nutritional value and price of different types of edible oils on the market vary a lot. Because of the spurious activities in the market, it is necessary to establish effective detection methods to classify the quality of the edible oils in the market. Traditional edible oil classification methods are usually time-consuming and requiring complex pre-treatment in the lab. Molecular spectroscopy can elucidate the sample information of both compositions and properties at the molecular level, and molecular spectra analysis has the advantages of fast speed detection and non-destructive testing for edible oil classification. Molecular spectra analysis combined with the chemometrics is becoming a popular method for rapid classification of edible oil. SIMCA (Soft Independent Modeling of Class Analogy) is widely applied to molecular spectra analysis. However, the Euclidean distance is used in SIMCA to classify the extracted features with PCA and F test. Therefore it is difficult to classify the irregular feature spaces. When the molecular spectral differences among the different types of samples are tiny such as edible oils, it is usually difficult to identify them with the traditional SIMCA method. SVDD（Support Vector Domain Description）algorithm is a support domain method for solving the one-class classification problem. SVDD can get a hypersphere to include as many objective samples as possible by solving the convex quadratic programming problem. In this work, a method of molecular spectra analysis based on SIMCA-SVDD method for rapid classification of edible oils is proposed. In order to accomplish recognition of the different types of edible oils, the attenuated total reflectance infrared spectra of four types of edible oil are scanned on ATR-FTIR. SIMCA is applied to extract the classification features T2 and Q. Since the extracted edible oil classification features T2 and Q distribute irregularly, instead of classification with Euclidean distance in SIMCA, Support Vector Domain Description (SVDD) is applied in this work to classify the extracted features. Since SVDD can map the extracted classification features to high dimensional space by mapping functions, then an optimal classification hypersphere can be trained to classify the irregular distributing feature spaces by solving the convex quadratic programming problem. Comparative experiments to identify the same molecular spectra samples with the proposed SIMCA-SVDD method and the SIMCA method have also been done. Comparative experiment results have verified that the classification results with the proposed SIMCA-SVDD method are obviously better than that with SIMCA. The proposed SIMCA-SVDD method has provided a new way to classify the edible oil rapidly based on molecular spectra analysis.