The rapid prediction of the low-carbon fatty acids (C≤14) content in grease samples was achieved by a mathematical model established by near infrared spectroscopy combined with support vector machine regression (SVR). In the present project, near-infrared spectrometer SupNIR-5700 was used to collect near-infrared spectra of 58 samples; partial least square (PLS) was applied to remove the strange samples, and principal component analysis (PCA) was conducted on the measurements; radial basis function (RBF) kernel function was selected to establish a regression model supporting vector machine, and then detailed analysis and discussions were conducted concerning their spectral preprocessing and parameters optimization methods. Experimental results showed that by applying particle swarm optimization (PSO) the model demonstrated improved performance, stronger generalization ability, better prediction accuracy and robustness. In the second pretreatment method after PSO, when the optimization parameters are: C=2.085, γ=22.20, the prediction set and calibration set correlation coefficient (r) reached 0.998 0 and 0.925 8, respectively; and root mean square errors (MSE) were 0.000 4 and 0.014 3, respectively. Research results proved that the method based on near infrared spectroscopy and PSO-SVR for accurate and fast prediction of the low-carbon fatty acid content in vegetable oil is feasible.