Article: Cloud computing based construction and empirical evaluation of the security risk early warning evaluation system of digital economy Journal: International Journal of Computer Applications in Technology (IJCAT) 2026 Vol.78 No.2 pp.135 - 143 Abstract: Traditional static risk assessment methods struggle to meet real-time processing demands for large-scale, multi-source heterogeneous data, showing sluggish responsiveness to emergencies and abnormal transactions. These approaches often suffer from poor early-warning accuracy and frequent false or missed alerts. To address these challenges, this study proposes a cloud-based security risk warning evaluation system for the digital economy. The system first establishes a multi-level risk indicator framework, utilising fuzzy hierarchical analysis and information entropy to calculate weighted metrics that integrate qualitative and quantitative indicators. It then employs grey prediction algorithms for short-term risk trend forecasting. Through a cloud computing distributed architecture, the system achieves real-time collection, processing and risk assessment of multi-source heterogeneous data, ensuring instant precision in warnings. Experimental results demonstrate that this method consistently outperforms existing approaches in both warning accuracy and recall metrics, with significantly reduced average response time, while maintaining reasonable control over false alarm rates and resource consumption. This research provides a practical technical solution for digital economy security risk management, offering both theoretical value and practical significance. Inderscience Publishers - linking academia, business and industry through research

Title: Cloud computing based construction and empirical evaluation of the security risk early warning evaluation system of digital economy

Authors: Yanan Wu

Addresses: Saxo Fintech Business School, Sanya University, Sanya, Hainan, China

Abstract: Traditional static risk assessment methods struggle to meet real-time processing demands for large-scale, multi-source heterogeneous data, showing sluggish responsiveness to emergencies and abnormal transactions. These approaches often suffer from poor early-warning accuracy and frequent false or missed alerts. To address these challenges, this study proposes a cloud-based security risk warning evaluation system for the digital economy. The system first establishes a multi-level risk indicator framework, utilising fuzzy hierarchical analysis and information entropy to calculate weighted metrics that integrate qualitative and quantitative indicators. It then employs grey prediction algorithms for short-term risk trend forecasting. Through a cloud computing distributed architecture, the system achieves real-time collection, processing and risk assessment of multi-source heterogeneous data, ensuring instant precision in warnings. Experimental results demonstrate that this method consistently outperforms existing approaches in both warning accuracy and recall metrics, with significantly reduced average response time, while maintaining reasonable control over false alarm rates and resource consumption. This research provides a practical technical solution for digital economy security risk management, offering both theoretical value and practical significance.

Keywords: risk early warning; evaluation system construction; digital economy; economic security.

DOI: 10.1504/IJCAT.2026.151720

International Journal of Computer Applications in Technology, 2026 Vol.78 No.2, pp.135 - 143

Received: 27 May 2025
Accepted: 20 Oct 2025
Published online: 17 Feb 2026 *

Title: Cloud computing based construction and empirical evaluation of the security risk early warning evaluation system of digital economy

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