Generative Model-Based Personalized Design of Non-Heritage Cultural and Creative Products and The Path of Remodeling Rural Cultural Resources
DOI:
https://doi.org/10.70917/ijcisim-2026-0010Keywords:
StyleGAN; Genetic Algorithm; Improved Interactive Genetic Algorithm; Non-heritage Cultural and Creative ProductsAbstract
This paper proposes a non-heritage cultural and creative generation product design effect image method based on StyleGAN. On this basis, the personalized design of non-heritage cultural and creative products is carried out, and the weights of the parameters are determined by adopting the method of interactive selection with the participation of target customers. The problem that traditional interactive genetic algorithm cannot effectively reduce user fatigue is improved by mutating individuals and genetic operation, and it is applied to multi-function product configuration design. The required initial shapes and inference rules are extracted from past products, and functional constraints are imposed on the product while obtaining a solution that meets the product design requirements. The results show that StyleGAN model is more effective for personalized design of non-heritage cultural and creative products, and its comprehensive satisfaction rate reaches 85.36%. When the number of iterations reaches 85, the fitness value of the non-heritage cultural and creative products generation model is gradually stabilized and finally reaches 1.5686, which shows that the model in this paper can obtain a satisfactory solution before reaching the maximum number of hereditary generations, proving that it is more effective in the derivation of non-heritage cultural and creative products. Finally, through the three forms of “extraction and innovation, reorganization and deformation, transplantation and grafting”, the cultural symbols are integrated into the design practice to create a brand of non-heritage cultural and creative products with the characteristics of rural cultural resources.
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Copyright (c) 2026 Jingjing Ai

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