Near-infrared spectroscopy (NIRS), combined with chemometrics methods, is applied to rapid quantitative determination of oleic acid and linolenic acid in camellia oil blends. 76 camellia oil samples are prepared and used for near-infrared spectral collection. Different spectral preprocessing methods are applied to effective information extraction. Two variable selection methods, Monte Carlo uninformative variable elimination (MCUVE) and variable combination population analysis (VCPA), are applied to select characteristic NIRS variables for the two fatty acids in camellia oil blends. Quantitative analysis models of the fatty acids are built using partial least-square regression. The results show that NWD1 st-MSC preprocessing can be used for optimization of near-infrared spectra of the two fatty acids in camellia oil blends. It is found that the VCPA method can greatly improve the precision of the model and compress the modeling variables. For the oleic acid model, the modeling variables decrease from 1501 to 7, the root-mean-square error of cross-validation and correlation coefficient of calibration are 1.107 and 0.984, respectively, and the root-mean-square error and correlation coefficient of prediction are 1.178 and 0.981, respectively. For the linoleic acid model, the modeling variables decrease from 1501 to 8, the root-mean-square error of cross-validation and correlation coefficient of calibration are 0.089 and 0.987, respectively, and the root-mean-square error and correlation coefficient of prediction are 0.105 and 0.982, respectively. NIRS combined with NWD1 st-MSC-VCPA-PLSR provides a quick and easy analysis method for measuring fatty acids in camellia oil blends.