Research on the Mechanism and Path of Enhancing the Efficiency of Smart Supervision in Live Streaming Economy Empowered by Artificial Intelligence Technology

Authors

  • Kebiao Yuan Ningbo University of Technology
  • Zhijian Xu Ningbo University of Technology

DOI:

https://doi.org/10.62177/amit.v1i6.870

Keywords:

Artificial Intelligence, Live Streaming Economy, Smart Supervision, Technological Empowerment, Mechanism and Path

Abstract

With the development of information technology and the transformation of consumer culture, the live streaming economy has been integrated into China's digital economic system, driving urban economic growth. However, it has also brought about regulatory issues such as information asymmetry, false advertising, difficulty in ensuring quality, and high costs of rights protection. This new economic form of virtualization, real-time, and cross-domain is facing enormous challenges. Traditional methods, such as manual sampling, reporting, and post-event tracing, are unable to meet the complex and ever-changing live streaming economic ecology. The powerful perception, recognition, and understanding capabilities of artificial intelligence provide new possibilities for building a scientific regulatory system in real-time, accurately, and efficiently. This article uses the theory of technological empowerment to explore the operational mechanism of artificial intelligence in regulating the live streaming economy, with a focus on the role of artificial intelligence in empowering live streaming economy regulation in four aspects: data intelligence, behavior recognition, risk warning, and intelligent decision-making. In addition, this article proposes a feasible path for building a smart regulatory system from three aspects: technology integration, institutional collaboration, and talent cultivation, providing relevant inspiration and reference for exploring the construction of a government platform collaborative governance mechanism and achieving modernization of platform economic governance in practice.

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References

Liu, Y., & Li, N. (2021). Legal regulation of false advertising in live streaming e-commerce. Intellectual Property, (5), 68–82.

Zhou, J. (2021). Difficulties and countermeasure innovations of supervision on e-commerce live streaming. China Business and Market, 35(8), 72–80.

Wang, N. (2025). The application and impact of big data and artificial intelligence in e-commerce operation mode. Journal of Commercial Economics, (2), 38–41. (Note: The original journal name was corrected from "Journal of Commercial Economic" to the standard name "Journal of Commercial Economics".)

Tang, J., & Tang, C. (2025). Innovation uncertainty and artificial intelligence regulatory innovation. Journal of Dongbei University of Finance and Economics, (5), 3–14.

Wang, B. (2021). The essence, logic and trend of livestreaming e-commerce. China Business and Market, 35(4), 48–57.

Zhu, W. (2022). Analysis of difficult issues in the supervision of online live streaming sales. Youth Journalist, (9), 84–86.

Hu, C., Chen, W., Zhou, Y., Chen, C., & Sun, S. (2023). Regulation mechanism of live streaming e-commerce based on evolutionary game theory. Journal of Management Sciences in China, 26(6), 126–141.

Hu, S. (2023). Food safety management in online live broadcast marketing. Food & Machinery, 39(12), 65–69.

Qin, X. (2024). Institutional ethical regulation: A study of essential traits and implementation of credit supervision for live streaming e-commerce advertising. Journal of Hubei University (Philosophy and Social Science), 51(5), 167–175.

Mei, A., & Hou, Z. (2021). Standardized governance of e-commerce live streaming in the era of “live streaming+”. E-Government, (3), 28–37.

Li, S., & Yang, Z. (2025). Identification of e-commerce fake reviews generated by artificial intelligence based on BERT-BiLSTM-GAT. Chinese Journal of Management, 22(3), 557–567.

Guan, Z., Du, J., Xue, Z., Wang, P., Pan, Z., & Wang, X. (2024). Personalized public safety event detection based on reinforcement federated GNN. Journal of Software, 35(4), 1774–1789.

Wei, B. (2025). Large scale network traffic anomaly behavior detection method based on LSTM. Industrial Control Computer, 38(8), 89–91.

Xiao, Z., Hu, D., & Wang, Z. (2023). Research on e-commerce governance based on credit system supervision—Take the live streaming industry as an example. Journal of Nanjing University of Science and Technology (Social Sciences), 36(2), 44–51.

Zou, M., Wang, F., Song, S., Yan, W., & Dai, X. (2024). “Intelligence+food regulation”: Development process, application status, and future direction. Journal of Food Science and Technology, 42(3), 1–10.

Wan, F. (2023). Strait and outlet of the governance rights on network live broadcast marketing platform. Studies in Law and Business, 40(2), 146–158.

Li, G., Mei, T., & Liang, Y. (2024). On the reward and punishment mechanism of collusion of live commerce platforms from the perspective of governmental regulation. Journal of Jiangsu University (Social Science Edition), 26(2), 100–112.

Li, P. (2025). A new path for cultivating environmental talents in universities in the era of artificial intelligence. Environmental Education, (8), 16–21.

Gao, Y., Wang, M., He, L., Huang, P., & Li, S. (2025). AIGC generates new quality talents cultivation. China Educational Technology, (7), 78–85.

Zhang, T., Zhang, S., & Hu, L. (2024). Multiple paths of the integration of innovation chain and talent chain in the urban artificial intelligence industry. Soft Science, 38(6), 1–6, 12.

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Section

Articles

DATE

Received: 2025-11-08
Accepted: 2025-11-13
Published: 2025-11-17