关键词: 多智能体系统, 审计问题定性, 人工智能, 法规推荐, 大数据审计

Abstract: Addressing the technical challenges of multidimensional feature analysis and dynamic regulation alignment in audit case qualification, this study develops an audit regulation recommendation system based on multi-agent collaboration. Conventional large language models exhibit three limitations in regulatory recommendation tasks: semantic discrepancies between legal texts and audit problem representations, insufficient knowledge currency due to dynamic regulatory updates, and inadequate multi-level reasoning capabilities under single retrieval strategies. To resolve these issues, a collaborative architecture is proposed, integrating specialized agents for problem extraction, logical reasoning, and pseudo-case generation. The task decomposition mechanism parses complex audit cases into parallelizable subproblem spaces. Methodologically, the framework innovatively combines instruction fine-tuning with Retrieval-Augmented Generation (Retrieval-Augmented Generation, RAG) technology, establishing a three-tier regulatory knowledge base covering national statutes, organizational policies, and internal control protocols. A dynamic agent-based retrieval strategy is incorporated to enhance precision. Experimental results demonstrate 30.56% direct clause matching accuracy in national regulation recommendations, outperforming ChatGPT-4 (13.89%) by 116%. Semantic evaluation metrics achieve 71.19% BERTScore and 20.20% Rouge-L. For multi-level regulatory recommendations, the system attains 63.91% accuracy, 26.50% BERTScore, and 27.80% Rouge-L, which exceeded the baseline model by at least 15.2%. The findings reveal that task decomposition through agent collaboration effectively disentangles multidimensional audit features, while the hierarchical knowledge architecture improves recommendation accuracy and provides interpretable regulatory reasoning paths. The modular design supports dynamic expansion of jurisdiction-specific regulation databases, demonstrating significant engineering applicability.

Key words: Multi-agent Systems, Audit Case Qualification, Artificial Intelligence, Regulation Recommendation, Big Data Audit