Abstract: To explore the regional distribution characteristics and differences of main chemical components of tobacco stem materials from different ecological and planting areas, the cured tobacco stems of grades B2F and C3F of the dominant flue-cured tobacco cultivar Yunyan 87 from 24 planting areas in four typical ecological areas (the Southwest Plateau, the Guizhou-Guangxi Mountain, the Wuling-Qinba and the Nanling Hill Areas) of China were used as test materials. The mass fractions of routine chemical components and cell wall substances were determined respectively. Inverse distance weighting interpolation, multiple comparison, paired-samples T-test, two-way analysis of variance and hierarchical clustering analysis were adopted for data analysis. The results showed that: 1) There were significant planting-area and stem-grade differences of mass fractions of the main chemical components of tobacco stems. Among which, planting area constituted the major source of differences in chemical properties of tobacco stems, while stem grade resulted in significant differences in the accumulation of saccharides, nitrogen-containing components and cell wall substances. 2) The interaction between planting area and stem grade exerted an extremely significant effect on all chemical indexes of tobacco stems (p< 0.01). The mass fractions of carbohydrates decreased from west to east planting areas. High chloride and potassium mass fractions were concentrated in Chuxiong and Dali of the Southwest Plateau, and Changsha of the Nanling Hill Areas, respectively. C3F tobacco stems accumulated more saccharides, while B2F tobacco stems possessed significantly higher mass fractions of total nitrogen, nicotine, chloride, cellulose and lignin than C3F. 3) A total of 48 tobacco stem samples from 24 planting areas were divided into five chemical-characteristic clusters, i.e., high nitrogen-alkali and low sugar, high lignin-cellulose-potassium and low nitrogen, high sugar and low chloride, high chloride and low potassium-nicotine according to the clustering analysis results.