Quantitative vegetation classification can be conducted using traditional approaches based on plant species composition and abundance; however, it may also be performed based on community functional traits to reflect the functional responses of vegetation to environmental changes. The latter approach has considerable potential for future development. This study classified and ordinated 24 woody plant communities on the southern slope of Beishan Mountain in Jinhua by integrating community-weighted mean functional traits with hierarchical cluster analysis (Ward method), detrended correspondence analysis (DCA), and canonical correspondence analysis (CCA). The resulting functional trait-based classification was further compared with traditional quantitative classifications based on importance values. The objectives of this study were to evaluate the feasibility of quantitative vegetation classification using functional traits and to assess its effectiveness in reflecting both environmental gradients and ecological functions of plant communities. Results indicated that the classification based on weighted community functional traits was broadly consistent with that derived from species composition. Both methods clearly distinguished Pinus taiwanensis forest, Cryptomeria japonica var. sinensis+Cunninghamia lanceolata forest, Schima superba forest, mixed S. superba+deciduous broadleaf forest, and Lindera glauca shrubland. However, the functional trait-based classification provided greater resolution, particularly in distinguishing secondary shrublands representing transitional stages between shrubland and forest. Notably, the C. japonica var. sinensis+C. lanceolata forest was further subdivided into pure C. japonica var. sinensis and C. lanceolata forests. DCA ordination revealed that species composition effectively captured vegetation changes along environmental gradients, including altitude, heat load index, soil organic carbon, potassium content, and pH. In contrast, ordination based on functional traits reflected additional, more nuanced functional characteristics. Combinations of leaf area, leaf dry matter content, leaf tissue density, bark thickness, and twig diameter revealed distinct ecological strategies related to investment-return trade-offs across vegetation types. CCA further demonstrated that functional trait-based ordination performs comparably to species composition-based ordination in capturing environmental variation. Environmental gradients such as altitude, soil organic carbon, chemical element contents, and heat load index showed significant associations with both community composition and functional traits. This study confirms the feasibility and effectiveness of using community functional traits for quantitative vegetation classification and ordination, enabling simultaneous representation of environmental conditions and ecosystem functions. These findings provide a theoretical foundation for the classification, conservation, and restoration of subtropical evergreen broad-leaved forests.