This research introduces a sophisticated technical assessment framework for complex information systems, based on Artificial Intelligence Generated Content (AIGC). It is specifically engineered to address the inherent challenges of scalability and auditability within public-sector digital infrastructures. By synthesizing semantic modeling, generative AI, and graph computing, the framework facilitates the automated conversion of extensive volumes of unstructured documentation into structured functional topology graphs. The core innovation lies in the deployment of AIGC for the rapid generation of domain-specific NLP analysis code, enabling deep semantic classification of multi-stakeholder requirements. Through graph similarity analysis between requirement-derived semantic models and legacy system topologies, the framework identifies "friction points" that hinder system evolution, thereby optimizing the robustness of technical upgrades. Empirical results indicate that this methodology significantly enhances assessment efficiency (reducing analysis time by 70%) and interpretational transparency. Ultimately, it provides a rigorous quantitative foundation for the standardized management of public information assets, cross-agency collaborative governance, and the resilient iteration of critical digital infrastructure.

🎯 The webinar will conclude with time for questions and discussion.

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