Objective To establish a method for authenticity identification of camellia seed oil.Methods The fatty acid composition of camellia seed oil and six common vegetable oils are determined by gas chromatography. Principal component analysis (PCA) is employed to explore the distribution patterns of fatty acids among the vegetable oils and to screen for potentially adulterated oils. Partial least squares discriminant analysis (PLS-DA) is applied to establish a model between fatty acid profiles and actual adulterated oils. Finally, multiple stepwise linear regression analysis is used to establish quantitative analysis models for adulteration in camellia seed oil.Results Among the six common vegetable oils, olive oil and peanut oil exhibit fatty acid compositions most similar to that of camellia seed oil, and are therefore selected as potential adulterants. The PLS-DA model could discriminate adulterated oils with adulteration levels no lower than 5%, and identify characteristic fatty acids for olive oil adulteration (palmitic acid, behenic acid, linoleic acid, palmitoleic acid and stearic acid) and for peanut oil adulteration (oleic acid, linoleic acid and palmitic acid). The quantitative models established by stepwise linear regression for olive oil and peanut oil adulteration in camellia seed oil achieve correlation coefficients (R²) greater than 0.9 for both models. The limits of detection (sensitivities) for the two models are 5% and 2%, respectively, and the method repeatabilities range from 0.7% to 6.0% and from 0.8% to 3.3%, respectively. The errors between predicted and actual values range from -1.93% to 2.08% and from -0.79% to 0.15%, respectively.Conclusion This comprehensive technical strategy, encompassing screening, qualitative discrimination, and quantitative analysis, provides a complete solution for the authenticity assessment of camellia seed oil.