Research on Tax Collection and Administration Application and Legal Issues Based on Big Data Analysis

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Abstract

In the twenty-first century, in the era of rapid development of big data, people can make some basic problem analysis of the application of tax collection and administration and legal problems through big data. Between taxpayers and the tax collection and management system, the law is used to protect the legitimate rights and interests of taxpayers. Through the improvement and management of the supervision system, it can ensure that taxpayers can clearly know how much tax they should pay when paying taxes. At present, the urgent problem is to adjust the concept of tax, reconstruct the scope of tax, scientifically design the tax rate, and simplify the tax collection and administration procedures. This paper puts forward a more efficient tax process by comparing the tax data, the econometric study of tax, and the secret tax avoidance while the burden of the more poor rural tax class. Through the experiment proved, this paper studies the best tax collection and management mode, so that taxpayers can pay taxes more convenient and fast. The tax collection and administration system have been improved to make taxpayers' information more comprehensive, which is very important for us to improve the efficiency in the twenty-first century, which is also a great breakthrough in the application of tax collection and administration. Due to the limitations of incomplete information, or that the information cannot keep up with the times, the tax collection and management system cannot completely correspond to the complete information of the correct taxpayers. Therefore, for the study of the taxation supervision system, it is necessary to use more algorithms in a more comprehensive way, so as to reduce the number of tax evaders and the number of active taxpayers.

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APA

Liuhong, C. (2022). Research on Tax Collection and Administration Application and Legal Issues Based on Big Data Analysis. Journal of Sensors. Hindawi Limited. https://doi.org/10.1155/2022/6578964

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