Objective: This study aimed to investigate different discriminant analysis models for pseudo-mixtures of walnut oil and rapeseed oil. Methods: Gas chromatography technology was used to analyze the fatty acid content in the adulterated mixture of walnut oil and rapeseed oil. Chemical stoichiometric methods were used to model the gas chromatography data, and discriminant analysis was performed on different proportions of walnut oil and rapeseed oil mixtures. Results: Pure walnut oil and adulterated walnut oil were distinguihed by using Principal Component Analysis (PCA) identified, and percentage of adulteration in the sample was calculated. 83.33% of the samples were successfully categorized using the Bayes discriminant analysis. Partial Least Squares Discriminant Analysis (PLS-DA) achieved 87.50% discrimination accuracy. Based on the BP neural network model for discriminant analysis, the accuracy of the training set was 84.21% and the accuracy of the test set was 80.00%. For both the training and testing sets, the genetic algorithm-based discriminant analysis using an optimized support vector machine (SVM-ga) achieved 100% accuracy. Conclusion: Multiple analytical models can identify the adulteration ratio of walnut oil and rapeseed oil to varying degrees, among which the SVM-ga model had the best prediction accuracy.