Research on the Construction and Performance Evaluation of an Intelligent Communication Model for Traditional Chinese Medicine Culture from the Perspective of Digital Empowerment
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Abstract
This study aims to construct a systematic and evaluable intelligent communication model for Traditional Chinese Medicine (TCM) culture under the framework of digital empowerment. As modern digital communication increasingly relies on electromagnetic information transmission, wireless networking technologies, and intelligent interactive infrastructures to support multimodal content delivery, the efficient dissemination of TCM culture requires an integrated architecture that combines data intelligence with adaptive communication strategies. By integrating communication studies, cognitive science, and information technology, a three-layer intelligent communication architecture consisting of a data support layer, intelligent content generation layer, and precise user interaction layer is proposed, together with a cross-layer governance and compliance mechanism. The key technical contribution lies in the development of a deep recommendation algorithm that incorporates domain knowledge graphs and in the establishment of a comprehensive performance evaluation framework based on the Analytic Hierarchy Process (AHP) and a multi-indicator weighted summation model. Validation using publicly available cases, including the “New Medical Hub” platform and an intelligent herbal dispensing system, demonstrates that the proposed framework effectively expands communication coverage, enhances cultural understanding, and promotes health behavior transformation. The study further identifies critical challenges associated with data standardization, interpretable algorithms, and cross-domain governance, while highlighting the potential of intelligent communication systems operating over electromagnetic information infrastructures to facilitate scalable and reliable digital cultural dissemination.
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References
W. Min, C. Liu, L. Xu, and S. Jiang, “Applications of knowledge graphs for food science and industry,” Patterns, vol. 3, no. 5, 100484, 2022, doi: 10.1016/j.patter.2022.100484.
L. Zhao, “Construction of a knowledge graph based on the study of English translation of traditional Chinese medicine terminology,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, 2024, doi: 10.2478/AMNS-2024-0564.
W. Chansanam, Y. Jaroenruen, N. Kaewboonma, and K. Tuamsuk, “Culture knowledge graph construction techniques,” Education for Information, vol. 38, no. 3, pp. 233-264, 2022, doi: 10.3233/EFI-220028.
F. Khosravi, G. Izbirak, and K. A. Adesina, “An exponentially distributed stochastic model for sustainability measurement of a healthcare system,” Sustainability, vol. 11, no. 5, pp. 1285, 2019, doi: 10.3390/su11051285.
A. Mostepaniuk, T. Akalin, and M. R. Parish, “Practices pursuing the sustainability of a healthcare organization: A systematic review,” Sustainability, vol. 15, no. 3, pp. 2353, 2023, doi: 10.3390/su15032353.
Z. Zeng, H. Zhang, T. Yu, S. Liu, L. Zhang, H. Gao, et al., “Review on the Research and Application of Knowledge Graph and its Key Technologies in the Field of Traditional Chinese Medicine,” Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology, vol. 24, no. 2, pp. 319-327, 2022, doi: 10.11842/wst.20210428006.
J. Li, “Research on search engine technology of knowledge graph analysis,” AIP Conference Proceedings, vol. 2073, no. 1, 020105, 2019, doi: 10.1063/1.5090759.
Z. W. Zhou, Y. H. Ting, W. R. Jong, S. C. Chen, and M. C. Chiu, “Development and application of knowledge graphs for the injection molding process,” Machines, vol. 11, no. 2, pp. 271, 2023, doi: 10.3390/machines11020271.
S. Liu, H. Yang, J. Li, and S. Kolmanič, “Preliminary study on the knowledge graph construction of Chinese ancient history and culture,” Information, vol. 11, no. 4, pp. 186, 2020, doi: 10.3390/info11040186.
Q. Yan, Y. Zhou, and L. Yin, “Analysis of the current situation and countermeasures of cross-border communication of Chinese medicine culture in the context of big data,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, 2024, doi: 10.2478/AMNS.2023.2.00612.
National Administration of Traditional Chinese Medicine and National Data Bureau, “Notice on Issuing "Several Opinions on Promoting the Development of Digital Traditional Chinese Medicine",” National Administration of Traditional Chinese Medicine Website; 19 July 2024, [Online]. Available: https://www.gov.cn/zhengce/zhengceku/202408/content_6968519.htm.
X. Ming, T. Zhiling, G. W. A. Dongshuo, Y. Long, M. Manhua, and X. Minjie, “Mapping Bozhou’s urban identity through multilingual media discourse: A big data study on the "World Capital of Traditional Chinese Medicine",” New Techno-Humanities, vol. 5, pp. 21-26, 2025, doi: 10.1016/j.techum.2025.10.007.
Guangdong Jiuan Pharmaceutical Service Co., Ltd., “Digital Empowerment of the "Last Mile" of TCM Inheritance—Recording China Traditional Chinese Medicine Holdings’ TCM Health Communication Practice,” Xinhua Net; 19 December 2025, [Online]. Available: http://www.xinhuanet.com/health/20251204/c6024efc978a4645882d003ea1a0c0b0/c.html.
S. Xiang, H. Lin, F. Cai, and Z. Jiang, “Integrating knowledge graphs with ancient Chinese medicine classics: Challenges and future prospects of multi-agent system convergence,” Chinese Medicine, vol. 20, no. 1, pp. 168, 2025, doi: 10.1186/s13020-025-01226-7.
National Data Bureau, “Second Batch of "Data Elements ×" Typical Cases No. 21: Full Process Data Fusion Accelerates the Inheritance and Innovation of Chinese Herbal Medicine Dispensing,” 1 July 2025, [Online]. Available: https://cqcs.gov.cn/zwxx_164/bmjz/202507/t20250703_14772965_wap.html.
X. Liu, “A review of explainable methods in medical artificial intelligence: Partial research overview [dissertation],” Anhui Province: University of Science and Technology of China; 2024, doi: 10.27517/d.cnki.gzkju.2024.002033.
S. Chen, X. Hu, A. Wu, B. Ji, and H. Zhu, “Digital empowerment in the dissemination of Chinese medicine culture: SWOT analysis and path selection,” Journal of Hunan University of Chinese Medicine, vol. 45, no. 2, pp. 356-362, 2025, doi: 10.3969/j.issn.1674-070X.2025.02.025.