Research on Cultural IP Digital Design Generation and User Acceptance Based on CNN and AIGC

Authors

  • Lunan Lin Taiyuan University of Science and Technology, Taiyuan, Shanxi, China Author

Keywords:

CNN, AIGC, cultural IP, design management, user acceptance

Abstract

This study aims to investigate the impact of digital design generation for cultural IPs based on Convolutional Neural Networks (CNN) and AI-generated content (AIGC) on user acceptance, whilst analyzing the moderating role of design management capabilities within this process. Dunhuang patterns were selected as cultural IP material, with design proposals generated through CNN feature extraction combined with the Stable Diffusion model. Hypothesis testing employed Structural Equation Modelling (SEM). Findings indicate that design management capabilities play a pivotal integrative role in the process of enabling cultural IP design generation through CNN and AIGC technologies. Establishing a human-machine co-creation design management pathway-guided by strategic positioning, underpinned by resource integration, and safeguarded by innovation management-constitutes the core approach to achieving the unification of technological and cultural value.

References

1. R. Abbott, "Intellectual property and artificial intelligence: an introduction," In Research Handbook on Intellectual Property and Artificial Intelligence, 2022, pp. 2-21. doi: 10.4337/9781800881907.00006

2. H. Lin, and W. Jia, "The Path of AIGC Helping Construct Cultural Digital IPs--Taking the Construction of Hemudu Digital Cultural IP as an Example," Cultural Arts Research and Development, vol. 5, no. 2, pp. 1-13, 2025.

3. Z. Yan, C. K. Lim, D. Hu, M. F. Ahmed, S. A. Halim, and L. Li, "Construction of digital dissemination effects evaluation indicator system of traditional techniques of intangible cultural heritage," npj Heritage Science, vol. 13, no. 1, p. 224, 2025. doi: 10.1038/s40494-025-01793-w

4. M. S. Elforgani, and I. Rahmat, "The Influence of Clients' Qualities on Green Design Performance of Building Projects in Malaysia-Descriptive Study," American Journal of Applied Sciences, vol. 9, no. 10, p. 1668, 2012.

5. B. Zheng, and S. X. Liu, "A Review of Research on Design Management and Dynamic Capabilities," In Congress of the International Association of Societies of Design Research, 2022, pp. 1793-1812. doi: 10.1007/978-981-19-4472-7_116

6. L. Chen, S. Li, Q. Bai, J. Yang, S. Jiang, and Y. Miao, "Review of image classification algorithms based on convolutional neural networks," Remote Sensing, vol. 13, no. 22, p. 4712, 2021. doi: 10.3390/rs13224712

7. L. A. Gatys, A. S. Ecker, and M. Bethge, "Image style transfer using convolutional neural networks," In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2414-2423. doi: 10.1109/cvpr.2016.265

8. F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS quarterly, pp. 319-340, 1989. doi: 10.2307/249008

9. Z. Huang, X. Fu, and J. Zhao, "Research on AIGC-Integrated Design Education for Sustainable Teaching: An Empirical Analysis Based on the TAM and TPACK Models," Sustainability, vol. 17, no. 12, p. 5497, 2025. doi: 10.3390/su17125497

10. C. Zott, and R. Amit, "Business model design: An activity system perspective," Long range planning, vol. 43, no. 2-3, pp. 216-226, 2010. doi: 10.1016/j.lrp.2009.07.004

11. W. Li, W. Zhang, W. Wu, and J. Xu, "Exploring human-machine collaboration paths in the context of AI-generation content creation: a case study in product styling design," Journal of Engineering Design, vol. 36, no. 2, pp. 298-324, 2025.

12. M. Ghobadi, S. Shirowzhan, M. M. Ghiai, F. Mohammad Ebrahimzadeh, and F. Tahmasebinia, "Augmented reality applications in education and examining key factors affecting the users' behaviors," Education Sciences, vol. 13, no. 1, p. 10, 2022. doi: 10.3390/educsci13010010

13. V. Gupta, R. Sadana, and S. Moudgil, "Image style transfer using convolutional neural networks based on transfer learning," International journal of computational systems engineering, vol. 5, no. 1, pp. 53-60, 2019.

14. Y. Cao, S. Li, Y. Liu, Z. Yan, Y. Dai, P. Yu, and L. Sun, "A survey of ai-generated content (aigc)," ACM Computing Surveys, vol. 57, no. 5, pp. 1-38, 2025. doi: 10.1145/3704262

Downloads

Published

2026-01-18

How to Cite

Lin, L. (2026). Research on Cultural IP Digital Design Generation and User Acceptance Based on CNN and AIGC. Simen Owen Academic Proceedings Series, 2, 246-257. https://simonowenpub.com/index.php/SOAPS/article/view/53