Abstract: In order to recognize the characteristic difference of flue-cured tobacco of different flavor types, 514 samples of flue-cured tobacco of fresh, robust and medium flavor types were collected, and 68 aroma components in tobacco samples were determined. By adopting data analysis and pattern recognition technology, an automatic recognition method for tobacco flavor type was proposed on the basis of tobacco aroma components and genetic algorithm-support vector machine (GA-SVM) algorithm. Genetic algorithm was used to optimize and adjust the parameters of support vector machine, and a 5-fold cross-validation method was used to calculate the classification accuracy of the proposed method. The classification results of GA-SVM, SVM and naive Bayesian algorithms were compared, the results showed that the flavor type discrimination accuracies of the three algorithms for the samples were 96.40%, 78.58% and 84.42%, respectively; GA-SVM was significantly more accurate than the other two algorithms. The proposed method provides a technical support for the accurate flavor type discrimination and growing area tracing of flue-cured tobacco.