{"title":"Evaluation method of e-government audit information based on big data analysis","authors":"Jingui He , Hansi Ya","doi":"10.1016/j.sasc.2025.200256","DOIUrl":null,"url":null,"abstract":"<div><div>With the continuous growth of e-government data, traditional audit methods face increasing limitations in handling large-scale data, leading to low processing efficiency and insufficient accuracy. To address these challenges, this paper proposes a big data-driven evaluation and prediction model for e-government audit information. The proposed method is built on a Hadoop-based distributed computing platform, which supports heterogeneous data integration and efficient parallel processing. Furthermore, a parallel PSO-RF algorithm combining Particle Swarm Optimization (PSO) and Random Forest (RF) is designed to enhance classification performance and computational efficiency. Experiments are conducted using e-government audit data from a Chinese province collected between 2018 and 2020, covering 15 audit categories. The model performance is comprehensively evaluated using accuracy, recall, F1-score, and AUC metrics. Results demonstrate that the proposed parallel PSO-RF algorithm outperforms conventional RF and Support Vector Machine (SVM) approaches across multiple indicators, with a maximum prediction deviation of only 0.28 % compared to actual audit issue probabilities. This study not only improves the accuracy and efficiency of audit information processing but also provides a scalable technical approach and theoretical foundation for intelligent audit evaluation and risk assessment in e-government systems.</div></div>","PeriodicalId":101205,"journal":{"name":"Systems and Soft Computing","volume":"7 ","pages":"Article 200256"},"PeriodicalIF":3.6000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems and Soft Computing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772941925000742","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With the continuous growth of e-government data, traditional audit methods face increasing limitations in handling large-scale data, leading to low processing efficiency and insufficient accuracy. To address these challenges, this paper proposes a big data-driven evaluation and prediction model for e-government audit information. The proposed method is built on a Hadoop-based distributed computing platform, which supports heterogeneous data integration and efficient parallel processing. Furthermore, a parallel PSO-RF algorithm combining Particle Swarm Optimization (PSO) and Random Forest (RF) is designed to enhance classification performance and computational efficiency. Experiments are conducted using e-government audit data from a Chinese province collected between 2018 and 2020, covering 15 audit categories. The model performance is comprehensively evaluated using accuracy, recall, F1-score, and AUC metrics. Results demonstrate that the proposed parallel PSO-RF algorithm outperforms conventional RF and Support Vector Machine (SVM) approaches across multiple indicators, with a maximum prediction deviation of only 0.28 % compared to actual audit issue probabilities. This study not only improves the accuracy and efficiency of audit information processing but also provides a scalable technical approach and theoretical foundation for intelligent audit evaluation and risk assessment in e-government systems.