IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Analysis of the Application of Information Technology in the Management of Rural Population Return Based on the Era of Big Data

Analysis of the Application of Information Technology in the Management of Rural Population Return Based on the Era of Big Data
View Sample PDF
Author(s): Zheng Cai (School of Public Administration and Law, Northeast Agricultural University, Harbin, China)
Copyright: 2022
Volume: 34
Issue: 3
Pages: 15
Source title: Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sang-Bing Tsai (Wuyi University, China; International Engineering and Technology Institute (IETI), Hong Kong)
DOI: 10.4018/JOEUC.286171

Purchase


Abstract

Based on rural population return management, governance theory, and information technology theory, this paper analyzes the specific performance of rural areas in managing population return, and describes the overview, quantity, life status, and demographic characteristics of rural population return, as well as the current situation of rural population return management. A method of managing rural population return based on a rural population return management model constructed by a machine learning algorithm is designed. The empirical results show that the method designed in this paper is low-cost, fast, and highly accurate, and is well suited for improving and expanding the system for managing rural return flows. The research in this paper provides a reference for further promoting the transformation strategy of rural governance in the context of new urbanization.

Related Content

Lele Qin, Guojuan Zhang, Li You. © 2022. 18 pages.
Yanmei Zhao, Yixin Zhou. © 2022. 17 pages.
Zheng Cai. © 2022. 15 pages.
Yu-Hsi Yuan, Yi-Cheng Yeh, Chia-Huei Wu, Cheng-Yong Liu, Hsin-Hao Chen, Chien-Wen Chen. © 2022. 15 pages.
Jianzu Wu, Kunxin Zhang. © 2022. 13 pages.
Yunhong Xu, Guangyu Wu, Yu Chen. © 2022. 17 pages.
Qihua Liu, Li Wang, Jingyi Zhou, Wei Wu, Yiran Li. © 2022. 26 pages.
Body Bottom