Research on the Architecture Design and Efficiency Evaluation of Multimodal Intelligent Accounting Systeme

Abstract

The deep integration of the digital economy and artificial intelligence has accelerated the transition of accounting systems from informatization and automation toward intelligence and multimodal processing. Traditional accounting systems are mainly designed for structured data, and therefore show limited ability to parse unstructured documents, images, audio, and video, as well as insufficient integration between business and financial data. This study designs a multimodal intelligent accounting system based on large language models, computer vision, speech recognition, knowledge graphs, intelligent agents, and data governance theory. A full-chain architecture of “perceptionunderstanding-reasoning-decision-making-interaction” is constructed, covering heterogeneous data ingestion, cross-modal alignment, semantic interoperability, conversational interaction, workflow automation, security control, and efficiency evaluation. The system treats OCR, ASR, and RPA as downstream tool plugins coordinated by a domain-specific LLM agent. It also considers the engineering requirements of secure wireless data transmission, electromagnetic-compatible terminal environments, and reliable networked accounting infrastructures. The proposed architecture provides theoretical and practical support for improving accounting efficiency, financial risk control, decision support, and enterprise digital transformation.