Leveraging Computational Intelligence for SDG-KPI Mapping and ESG Data Integration: A Technology-Driven Framework for Sustainable Accounting Disclosure

In corporate sustainable development practice, how to accurately align independently disclosed key performance indicators (KPIs) with the United Nations Sustainable Development Goals (SDGs) has long faced challenges such as ambiguous standards and complex operations. This research develops an intelligent analysis model. By integrating natural language processing and knowledge graph technology, it automatically maps ESG disclosure data and SDGs. The semantic analysis system, built from 200 cross-industry ESG reports, applies a text vectorization algorithm and dynamic weight adjustment mechanism, achieving a matching accuracy of 91% in identifying environmental governance indicators and a coverage rate of 86% in social indicators. The model innovatively introduces the GRI standard knowledge graph, effectively solving the problem of differences in information disclosure standards across industries. This system provides audit institutions with automated verification tools, assists regulators in establishing dynamic monitoring mechanisms, and promotes the transformation of companies' ESG practices from formal compliance to substantive innovation. The research results have practical value in breaking the current fragmented situation of information disclosure for sustainable development and provide technical support for building a reliable global accountability governance system.