<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the past two years, with the development of big data technology and the revolutionary innovations it has caused within various industries, there has been a boom in the use of big data to transform and optimize government workflow within the field of public administration. In this paper, we build a theoretical framework through government statistics, focusing on the four elements of government statistics. The decision tree algorithm is used to analyze the indicators of the government statistical work problem, mainly from three aspects: grassroots statistical institutions, government revenue, and government forestry construction statistics. The accuracy of the decision tree algorithm is controlled between 83% and 91%, with a variation range value of 8%, which is more accurate than the other two algorithms. The decision tree algorithm helps improve the efficiency and quality of government statistics, thus contributing to the processing of statistical work problems and reform strategies. This study is important for deepening the reform of the Chinese government administration system, improving the construction of a service-oriented government, ensuring the quality of statistical information, and guiding the benign development of statistical work.</p>
</abstract>

In the past two years, with the development of big data technology and the revolutionary innovations it has caused within various industries, there has been a boom in the use of big data to transform and optimize government workflow within the field of public administration. In this paper, we build a theoretical framework through government statistics, focusing on the four elements of government statistics. The decision tree algorithm is used to analyze the indicators of the government statistical work problem, mainly from three aspects: grassroots statistical institutions, government revenue, and government forestry construction statistics. The accuracy of the decision tree algorithm is controlled between 83% and 91%, with a variation range value of 8%, which is more accurate than the other two algorithms. The decision tree algorithm helps improve the efficiency and quality of government statistics, thus contributing to the processing of statistical work problems and reform strategies. This study is important for deepening the reform of the Chinese government administration system, improving the construction of a service-oriented government, ensuring the quality of statistical information, and guiding the benign development of statistical work.

Research on the problems and reform strategies of government statistics based on big data technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the past two years, with the development of big data technology and the revolutionary innovations it has caused within various industries, there has been a...

Research on the problems and reform strategies of government statistics based on big data technology

Published Online: Apr 28, 2023

Page range: -

Received: Feb 21, 2022

Accepted: Jul 17, 2022

DOI: https://doi.org/10.2478/amns.2023.1.00061

Keywords
Big data technology, Decision tree algorithm, Information gain, Government statistics, Reform strategy

© 2023 Xianwei Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the problems and reform strategies of government statistics based on big data technology

Published Online: Apr 28, 2023

Page range: -

Received: Feb 21, 2022

Accepted: Jul 17, 2022

DOI: https://doi.org/10.2478/amns.2023.1.00061

KeywordsBig data technology, Decision tree algorithm, Information gain, Government statistics, Reform strategy

© 2023 Xianwei Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Big data technology, Decision tree algorithm, Information gain, Government statistics, Reform strategy