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The accuracy of classification models using spectral data decreases significantly with the increase numbers of categories. In order to overcome this problem, a middle layer is built based on the main factors of the sample. Herein, ten indicators, including chemical components, position and aroma styles, were selected to determine the identification of geographical origin and grade of flue-cured tobacco. Chemometrical algorithms were used to build quantitative prediction models based on labeled data. A voting algorithm was performed to determine the most likely geographical origin and grade of unknown samples. Experimental results show that the proposed method provides outstanding results for the independent test samples compared with traditional classify methods such as SIMCA and PLS-DA. The proposed method can be useful for origin traceability or adulteration detection of various agricultural products.