LLM-Based Semantic Transformation for Construction Cost-Revenue

As a pivotal pillar of the national economy, the construction industry has long grappled with dual challenges in cost management efficiency and precision. Traditional approaches relying on manual expertise for list decomposition and expense alignment suffer from process redundancies, semantic misinterpretations, and fragmented data collaboration. This paper proposes an intelligent solution leveraging Large Language Models (LLMs), which innovatively integrates multimodal data parsing, hierarchical dynamic matching strategies, and domain knowledge enhancement to automate and refine construction project cost management. Experimental validation demonstrates that the method significantly improves the accuracy and operational efficiency of cost management at the project level.