To guide the oriented design of the functional module in a certain cigarette brand from China Tobacco Jiangsu Industrial Co. Ltd., sensory analysis and chemical analysis were applied on the 236 tobacco samples from the storage. According to the use character, 4 functional groups including the group for increasing the quality and quantity of the aroma, the group for enhancing the concentration and strength, the group for balancing the cigarette smoke and the group for filling were classified based the 216 tobacco samples already used in the cigarette blend. Ninety–six representative tobacco samples from these 4 functional groups were selected, and the remaining 120 tobacco samples were optimized by using K–Nearest Neighbor method to make sure all the 216 tobacco samples can be divided into the 4 functional groups. Stepwise discriminant analysis was applied to establish the functional recognition model. The results showed that seven indexes including density of smoke, quantity of aroma, diffusiveness, softness, dryness, aftertaste and total alkaloids were used in the recognition functions and the correct rate of self–verification method and leave one interaction test method were 93.1% and 92.6%, respectively. Twenty new tobacco samples tend to be used in the cigarette blend were predicted by the functional recognition model, and the discriminant accuracy reached 100%.