In the context of increasingly strengthened digital regulation, traditional compliance investigation methods that rely on manual review are no longer able to cope with high-frequency risks and dynamic changes in complex multi-source data environments. This article proposes the construction of a compliance investigation framework that integrates big data engineering and intelligent analysis technology. It systematically outlines the conceptual paradigm and core content of compliance data, designs a data platform architecture with multi-source integration, trustworthy construction, and security management functions, and constructs an intelligent analysis system that integrates behavior recognition, rule generation, and warning decision-making. This study contributes to improving compliance efficiency, enhancing risk prevention and control capabilities, and providing technical support and path references for the digital transformation of organizational compliance governance.

Zhen Zhong. Big Data Engineering and Intelligent Analysis Framework for Compliance Investigation. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 11: 107-115. https://doi.org/10.25236/AJCIS.2025.081112.

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