Abstract: This study aims to reveal interspecific differences in biomass allocation strategies among Quercus (oak) species in Hunan Province, resolve the issue of component incompatibility in traditional models, and provide a measurement basis for the precise monitoring of regional forest carbon stocks. Taking five Quercus species—Q. variabilis, Q. acutissima, Q. myrsinifolia, Q. gilva, and Q. glauca—as research subjects, and based on data from 127 destructively sampled trees, compatible biomass model systems for both individual species and a generalized group were constructed. This was achieved using a mixed-variable design and the proportional adjustment method under total biomass control. Leave-One-Out Cross-Validation (LOOCV) was employed to evaluate the generalization ability of the models and to analyze the evolutionary patterns of component allocation. The results indicated that: (1) While the allometric growth rates remained biologically consistent across species, there were extremely significant interspecific differences in baseline biomass ( P< 0.01 ), confirming the necessity of species-specific modeling. (2) The mixed-variable compatible system effectively resolved the issue of component non-additivity. The coefficients of determination ( R^2 ) for total aboveground biomass and stem wood both exceeded 0.97. LOOCV results showed that the generalized model possessed excellent unbiasedness at the population scale (Mean Relative Error, MRE = 7.40%), whereas the specific models for Q. variabilis and Q. gilva significantly outperformed the generalized model in individual prediction accuracy. (3) As the diameter at breast height (DBH) increased, the proportion of stem biomass increased significantly across species, while the proportion of leaf biomass shrank sharply; the proportion of bark biomass remained stable between 8% and 15%. Notably, Q. acutissima exhibited a unique "heavy crown, light stem" allocation strategy, with its branch proportion rising to over 25% as the diameter class increased. The mixed-variable compatible models constructed in this study balance the biological characteristics of different components with statistical robustness. It is recommended to use the generalized model for regional-scale forest resource inventories in Hunan Province to ensure unbiased estimation, while species-specific models should be prioritized for precise accounting in specific forest stands.