A Hybrid Knowledge-Enhanced Legal AI System for Construction Contract Disputes

Abstract

Abstract With rapid urbanization and expanding infrastructure, construction contract disputes are increasing in volume and complexity, challenging traditional adjudication. This study proposes a domain-specific legal artificial intelligence (AI) system for construction contract disputes via hybrid knowledge integration based on the retrieval-augmented generation (RAG) paradigm, integrating five core legal texts and 500 adjudication cases within a dual-engine architecture. The knowledge base encodes legal concepts, relations, and rules to enable structured semantic inference. The DeepSeek-R1 reasoning engine analyzes case facts and legal logic via constrained generation, while the BGE-M3 retrieval module matches legal provisions and precedents using multivector indexing. A tripartite evaluation framework—semantic similarity, legal provision citation accuracy, and issue prediction F1 score—validates system performance. The hybrid knowledge model outperforms single-source models, achieving scores of 0.736, 0.952, and 0.937, respectively, while significantly reducing judicial document generation time. This study offers a theoretical and empirical basis for legal AI in Chinese construction disputes, demonstrating how integrating diverse legal knowledge enhances intelligent judicial assistance within China’s jurisdiction. It also provides a scalable methodological reference for the advancement of smart justice, with explicit recognition of its current jurisdictional limitations.