Research on the use of Near-infrared Spectroscopy for rapid predict oil fatty acid content, proposed a method based on fatty acid content to identify the oil species. Collect five kinds of 133 parts of edible oil data samples by Near-infrared spectroscopy. The original spectroscopy were pretreated by using standard normal variable variation and De-trending(SNV-DT), and using support vector machine regression (SVR) build quantitative models of fatty acids, using Support Vector Classification (SVC) to establish the type of oil qualitative model. Results show, Using palmitic acid, oleic acid, linoleic three kinds of fatty acids it is feasible. Three kinds of fatty acid quantitative model prediction set correlation coefficients were 95.0876%,99.8592% and 98.5951%. Quantitative - Qualitative model prediction accuracy rate of 100% set. Studies shows that Near-infrared spectroscopy can quickly predict oil fatty acid content, and discriminating oil species. This research has a strong practical and popularization value. In order to develop a kind of rapid method which predict fatty acid content and use it to identify the oil species to provide technical support.