Author(s)

XinChen Bian, XinYu Yang, YuanChen Yin, GuangSen Li^*

Affiliation(s)

Department of Management Science and Engineering, Qingdao University of Technology, Linyi 273400, Shandong, China.

Corresponding Author

GuangSen Li

ABSTRACT

This study uses big data analysis, cloud computing, artificial intelligence and other technologies to build a digital cloud platform for tax collection and management of anchor reward income, and provides full-process intelligent ‘cloud’ tax services. By analysing the current situation of the development of the network broadcasting industry and the problems of tax collection and management. A detailed design and implementation of the digital cloud platform for tax collection and management of anchor reward income is carried out, including the hybrid cloud module, data sharing module, and cloud service module for tax collection and management policies. It achieves full digital tracking and monitoring of anchor income, real-time collection and integration of data, accurate calculation and analysis, timely warning of abnormal situations, identification of potential tax avoidance, and accurate prevention and control of the risk of tax payment by anchors; it achieves the goals of fairness, compliance, efficiency, intelligence, and digitalisation of tax collection and administration of anchor bounty income, prevents tax source loss, helps coordinate the scheduling of tax policies, resources, information, and data, and provides intelligent decision support for the taxation It provides intelligent decision-making support for the tax department, and realises the standard tax collection and efficient control of anchor reward income.

KEYWORDS

Anchor bounty income; Tax administration cloud platform; Risk warning; Big data analysis

CITE THIS PAPER

XinChen Bian, XinYu Yang, YuanChen Yin, GuangSen Li. Building a digital cloud platform for tax collection and management of anchor reward income based on big data analysis and cloud computing. World Journal of Management Science. 2025, 3(1): 36-41. DOI: https://doi.org/10.61784/wms3055.

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