The Audit Application of Deep Learning Model: Working Mechanism, Function Expansion and Process Framework

Based on the technical advantages of deep learning models in multimodal data recognition, predictive analysis and decision support, semantic understanding and content generation, this study systematically reconstructs their functional architecture and application pathways in the audit field. Considering the high dependence of deep learning models on data-driven features, the study begins with the continuous iterative updating of audit data warehouses. It constructs an integrated application framework covering the full audit process—including planning, implementation, and reporting phases—to promote the deep integration of the intelligent self-learning mechanisms of deep learning with the professional knowledge and experience of human auditors. This integration aims to achieve synergistic effects between machine intelligence and human intelligence in uncertain judgment, risk identification, and evidence evaluation. In response to practical challenges such as high technical investment, high system complexity, and implementation difficulties that deep learning models may face in audit practice, the study proposes differentiated coping strategies from the perspectives of three core stakeholders in the audit ecosystem: accounting firms, regulatory bodies, and auditors. The study aims to provide theoretical guidance and practical insights for deepening the trustworthy, controllable, and sustainable application of artificial intelligence technology in audit scenarios.