{"title":"Risk Prediction of the Development of the Digital Economy Industry Based on a Machine Learning Model in the Context of Rural Revitalization","authors":"Rui Luan, Ping Xu","doi":"10.4018/irmj.343095","DOIUrl":null,"url":null,"abstract":"In today's society, rural areas face challenges such as complex terrain and uneven population distribution, and infrastructure construction is exceptionally difficult. At the same time, poor information transmission and low communication efficiency have also become a major obstacle to the promotion of the digital economy in rural areas. This study aims to use gradient advancement models to identify potential risks in the growth of the digital economy sector related to rural revitalization. In this study, we used an enhanced hierarchical gradient boosting algorithm. The research results indicate that the introduction of this technology can provide us with a more comprehensive and reliable risk prediction model, thereby more scientifically assisting the development and decision-making of the digital economy in rural areas. This article provides a new perspective and solutions for development issues in rural areas, promoting sustainable development and economic growth in rural areas.","PeriodicalId":44735,"journal":{"name":"Information Resources Management Journal","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Resources Management Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/irmj.343095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In today's society, rural areas face challenges such as complex terrain and uneven population distribution, and infrastructure construction is exceptionally difficult. At the same time, poor information transmission and low communication efficiency have also become a major obstacle to the promotion of the digital economy in rural areas. This study aims to use gradient advancement models to identify potential risks in the growth of the digital economy sector related to rural revitalization. In this study, we used an enhanced hierarchical gradient boosting algorithm. The research results indicate that the introduction of this technology can provide us with a more comprehensive and reliable risk prediction model, thereby more scientifically assisting the development and decision-making of the digital economy in rural areas. This article provides a new perspective and solutions for development issues in rural areas, promoting sustainable development and economic growth in rural areas.
期刊介绍:
Topics should be drawn from, but not limited to, the following areas, with major emphasis on the managerial and organizational aspects of information resource and technology management: •Application of IT to operation •Artificial intelligence and expert systems technologies and issues •Business process management and modeling •Data warehousing and mining •Database management technologies and issues •Decision support and group decision support systems •Distance learning technologies and issues •Distributed software development •E-collaboration •Electronic commerce technologies and issues •Electronic government •Emerging technologies management