Evaluation method of e-government audit information based on big data analysis

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.