AI-enhanced Chinese style furniture design: Integrating CNN cultural recognition with latent diffusion generation

Authors

• Yupeng Jiang, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow
• Jiayong Zheng, Hubei University of Technology, Wuhan, 430068, ChinaFollow
• Bingchen Gou, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow
• Jie Song, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Sustainable Building and Environmental Research Institute, Northwestern Polytechnical University, Xi’an, 710129, China;Follow
• Jianing Kang, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow
• Zhaoxian Ren, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow
• Ying Liu, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow
• Lingwei Lyu, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow
• Junyan Lin, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow
• Suyao Wei, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow
• Weilan An, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China; Key Laboratory of Ministry of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi’an 710072, ChinaFollow

Abstract

In the wave of artificial intelligence development, employing computer technology to advance the innovative design of Chinese-style furniture and optimize the intelligent development of cultural products is crucial for revitalizing the industry and strengthening cultural confidence. This study identifies issues of homogenization and cultural dilution in intelligent furniture design and proposes an intelligent design model based on convolutional neural networks (CNNs) and a latent diffusion model (LDM). CNNs are used to recognize cultural features in Chinese-style furniture, while the LDM generates design schemes, rapidly producing Chinese chairs that meet contemporary aesthetic preferences while retaining traditional cultural attributes. The study enhances the intelligent recognition and application of fine cultural details, avoids weakened aesthetic value caused by single-algorithm approaches, improves the efficiency of intelligent design for Chinese-style furniture, and offers new perspectives for the creative development of cultural products.

Keywords

Artificial Intelligence; Chinese-style furniture; Convolutional neural network; Latent diffusion model

DOI

https://doi.org/10.21606/drs.2026.964

Citation

Jiang, Y., Zheng, J., Gou, B., Song, J., Kang, J., Ren, Z., Liu, Y., Lyu, L., Lin, J., Wei, S., and An, W. (2026) AI-enhanced Chinese style furniture design: Integrating CNN cultural recognition with latent diffusion generation, in Simeone, L., Gray, C. M., Verhoeven, A., de Götzen, A., Bakırlıoğlu, Y., Zohar, H., Stead, M., and Buwert, P. (eds.), DRS2026: Edinburgh, 8–12 June, Edinburgh, United Kingdom. https://doi.org/10.21606/drs.2026.964

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License