A Study on the Design Translation of Majiayao Polychrome Pottery Water Sports Equipment Integrating Affective Engineering and AI-Generated Content

DOI: https://doi.org/10.62517/jike.202604122

Author(s)

Hongxia Deng

Affiliation(s)

School of Fine Arts and Design, Lanzhou University of Arts and Science, Lanzhou, Gansu, China

Abstract

This study aims to explore systematic pathways for generative AI (AIGC) to empower innovative digital design in cultural heritage preservation. Taking Majiayao painted pottery culture as the research subject, it integrates affective engineering with AIGC technology to construct a design translation framework spanning from cultural element perception to cultural and creative product generation. First, a database of painted pottery patterns was constructed, and core sensory factors were extracted. Second, the LoRA model was used to fine-tune Stable Diffusion, enabling precise control over the painted pottery style. Third, the Analytic Hierarchy Process (AHP) was employed to quantify user preference weights for cultural and product characteristics, mapping these to prompt parameters in the AIGC generation process. Ultimately, this approach generated the “Pottery Pattern Rhythm - Water-Inspired Elegance” series of aquatic sports equipment designs, including competitive swimwear, kayaks, and surfboards. User acceptance was validated through fuzzy comprehensive evaluation. This research provides a reference paradigm with both theoretical value and practical significance for the living inheritance and innovative transformation of intangible cultural heritage.

Keywords

Affective Engineering; AIGC; Majiayao Culture; Water Sports Equipment; LoRA Model; Design Translation

References

[1] Yang Yang. The Internal Mechanisms, Practical Challenges, and Development Strategies for the Creative Transformation and Innovative Development of Historical and Cultural Heritage: A Case Study of Henan Province. Henan Social Sciences, 2025, 33(07): 10-17. [2] Chen Changfeng, Chen Kaining. Generative Cultural Heritage: AIGC-Driven Innovation in Traditional Cultural Inheritance. Huaxia Culture Forum, 2025, (02): 195-205. [3] Anantrasirichai N, Bull D. Artificial Intelligence in the Creative Industries: A Review. Artificial Intelligence Review, 2022, 55(1): 589-656. [4] Wang H T. The Construction of Feature Symbols in Product Identity Recognition for Mining Machinery. Design, 2016(16): 134-136. [5] Wang N W, Wang J S, Bi Y F, et al. Application and Trends of Artificial Intelligence in Aesthetic Engineering Research. Packaging Engineering, 2023, 44(16): 32-40. [6] Jia Danping, Jin Jian, Geng Qian, et al. Research on User Demand Mining from an Aesthetic Engineering Perspective. Journal of Information Science, 2020, 39(3): 308-316 [7] Kang Xinhui. Research on Emotional Design of Product Forms Based on Fuzzy QFD. Shanghai: East China University of Science and Technology, 2019. [8] Xuefang Xie, Xinle Zhu. Innovation Research on Digital Cultural Production Models Empowered by AIGC in the “Intelligence+” Era. Fujian Forum (Humanities and Social Sciences Edition), 2023, (08): 16-29. [9] George A. S., George A. H. A Review of ChatGPT: AI's Impact on Several Business Sectors. Partners Universal International Innovation Journal, 2023, 1:9-23. [10] Chen Changfeng, Chen Kaining. Generative Cultural Heritage: AIGC-Driven Innovation in Traditional Cultural Inheritance. Huaxia Culture Forum, 2025, (02):195-205. [11] Liu Bing, Guan Ya. Research on Intangible Cultural Heritage Creative Design Based on AIGC Technology: Taking Huaiyang Mud Dogs as an Example. Design Art Research, 2025, 15(03): 94-99. [12] Yin Yukun, Chen Hong, Zhao Haiying. Application of Artificial Intelligence in Art Design. Packaging Engineering, 2020, 41(6): 252-261. [13] Ramdurai B, Adhithya P. “The Impact, Advancements, and Applications of Generative AI.” International Journal of Computer Science and Engineering, 2023, 10(6): 1-8. [14] Song Yu, Qian Xiaosong, Peng Lingyu, et al. Research on Chinese Aesthetic Cultural and Creative Design in AIGC. Packaging Engineering, 2023, 44(24): 1-8, 33.