Exploring the Role of Artificial Intelligence Technology in Enhancing Public Services in the Field of E-Government
Keywords:
Artificial intelligence, E-government, Public service efficiency, Governance capacity, Technological transformation, Intelligent decision-making, Government data sharing, Administrative reformAbstract
As a pivotal catalyst driving the new wave of technological revolution and industrial transformation, artificial intelligence (AI) has emerged as a critical enabler for economic and social development, fundamentally reshaping government administration and public service delivery models. In the domain of e-government, AI technologies provide comprehensive capabilities including rapid data perception, intelligent decision-making support, and automated process execution. These technological advancements are instrumental in optimizing administrative approval workflows, advancing the standardization of government services, facilitating open data sharing initiatives, and significantly enhancing government decision-making capacity. The innovative application of AI in e-government systems operates at multiple levels to create transformative effects. At the technical level, AI-driven solutions improve service quality through intelligent automation and predictive analytics. Institutionally, these technologies facilitate government function transformation by enabling data-driven policy formulation and administrative reform. At the safeguard level, AI applications contribute to enhanced social governance capacity through improved risk assessment and crisis management capabilities. The integration of AI into e-government systems thus effectively increases the quality and efficiency of public service delivery, while simultaneously providing more convenient and efficient service options for both enterprises and citizens. This technological transformation represents a significant leap forward in modernizing governance structures and optimizing public service provision.
References
Li, X., Lin, Y., & Zhang, Y. (2025). A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy. arXiv preprint arXiv:2507.12098.
Tu, Tongwei. "ProtoMind: Modeling Driven NAS and SIP Message Sequence Modeling for Smart Regression Detection." (2025).
Xie, Minhui, and Boyan Liu. "EvalNet: Sentiment Analysis and Multimodal Data Fusion for Recruitment Interview Processing." (2025).
Zhu, Bingxin. "ReliBridge: Scalable LLM-Based Backbone for Small Business Platform Stability." (2025).
Zhang, Yuhan. "Learning to Advertise: Reinforcement Learning for Automated Ad Campaign Optimization for Small Businesses." (2025).
Tan, C. (2024). The Application and Development Trends of Artificial Intelligence Technology in Automotive Production. Artificial Intelligence Technology Research, 2(5).
Zhuang, R. (2025). Evolutionary Logic and Theoretical Construction of Real Estate Marketing Strategies under Digital Transformation. Economics and Management Innovation, 2(2), 117-124.
Han, X., & Dou, X. (2025). User recommendation method integrating hierarchical graph attention network with multimodal knowledge graph. Frontiers in Neurorobotics, 19, 1587973.
Yang, Zhongheng, Aijia Sun, Yushang Zhao, Yinuo Yang, Dannier Li, and Chengrui Zhou. "RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders." arXiv preprint arXiv:2508.05289 (2025).
Zhang, Jingbo, et al. "AI-Driven Sales Forecasting in the Gaming Industry: Machine Learning-Based Advertising Market Trend Analysis and Key Feature Mining." (2025).
Yang, Yifan. "Web Front-End Application Performance Improvement Method Based on Component-Based Architecture." International Journal of Engineering Advances 2.2 (2025): 24-30.
Cheng, Ying, et al. "Executive Human Capital Premium and Corporate Stock Price Volatility." Finance Research Letters (2025): 108278.
Chen, J., Zhang, X., Wu, Y., Ghosh, S., Natarajan, P., Chang, S. F., & Allebach, J. (2022). One-stage object referring with gaze estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5021-5030).
Tong, Kejian, et al. "An Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction." 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024.
Q. Tian, D. Zou, Y. Han and X. Li, "A Business Intelligence Innovative Approach to Ad Recall: Cross-Attention Multi-Task Learning for Digital Advertising," 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shenzhen, China, 2025, pp. 1249-1253, doi: 10.1109/AINIT65432.2025.11035473.
Chen, Yinda, et al. "Generative text-guided 3d vision-language pretraining for unified medical image segmentation." arXiv preprint arXiv:2306.04811 (2023).
Zhang, Zongzhen, Qianwei Li, and Runlong Li. "Leveraging Deep Learning for Carbon Market Price Forecasting and Risk Evaluation in Green Finance Under Climate Change." Journal of Organizational and End User Computing (JOEUC) 37.1 (2025): 1-27.
Peng, Qucheng, Ce Zheng, and Chen Chen. "A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.
Pinyoanuntapong, Ekkasit, et al. "Gaitsada: Self-aligned domain adaptation for mmwave gait recognition." 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS). IEEE, 2023.
Zheng, Ce, et al. "Diffmesh: A motion-aware diffusion framework for human mesh recovery from videos." 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025.
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