The traditional financial statement generation process has many manual interventions, low efficiency and lagging rules adaptation. This paper proposes an automatic financial statement generation system that integrates natural language processing (NLP) and multi-agent system (MAS). The system adopts hierarchical and distributed architecture, realizes semantic understanding and structural mapping of unstructured financial texts through NLP layer, and constructs BERT-BiLSTM-CRF model to improve the recognition accuracy of amount entities; By introducing MAS cooperation mechanism, a rule adaptation algorithm based on utility function and reinforcement learning is designed to realize intelligent decision-making and multi-source data collaborative processing under the dynamic update of accounting standards. The experiment is based on the monthly financial data of real enterprises. The results show that the system completes the traditional report generation task that takes 4 hours within 15 minutes, and the error rate of account classification is reduced to 2.4%. The error rate of the first operation after the rule update is only 3.5%, which is significantly better than the traditional method and the single NLP scheme, and verifies its comprehensive advantages in efficiency, accuracy and adaptability.
Yue Yu (2026) studied this question.