Cross cultural analysis of brand color emotions based on federal CNN transformer and cultural calibration

The role of color emotion in brand visual identity influences consumer perception across cultures. Current design approaches encounter limitations in emotion quantification, cross-cultural adaptation, and privacy preservation. This research proposes a federated learning system combining CNN and Transformer architectures, where local processing extracts multi-scale color-texture patterns while global analysis models style semantics through attention mechanisms. An adaptive feature fusion strategy coordinates these complementary representations. The framework incorporates a cultural adaptation component that modulates emotion weights according to regional esthetic characteristics, accommodating diverse preferences including Middle Eastern gold symbolism and East Asian compositional harmony. Operating under distributed learning principles, this approach shows improved cross-cultural consistency and practical scalability compared to conventional methods. Evaluations indicate enhanced performance in emotion interpretation and color adaptation, with reduced cultural bias relative to centralized alternatives. The system shows stable operation across varied environmental and cultural conditions, supporting responsive brand identity development. These findings contribute to advancing AI-assisted design methodologies while addressing global–local integration challenges in brand strategy.