Research on Financial Risk Early Warning for Agricultural Enterprises Based on BP Neural Network

This study aims to establish a financial risk early warning model for agricultural listed companies, utilizing BP neural networks, to enhance the risk management capabilities of agricultural enterprises. The research focuses on Chinese agricultural listed companies that were first labeled as “ST” between 2017 and 2023, constructing a financial risk early warning indicator system and conducting empirical analysis. During the research process, five major financial factors were screened through factor analysis and employed as input layer nodes. Subsequently, the BP neural network model was constructed using MATLAB R2023a software for training and validation. The final research results demonstrate that the model exhibits high accuracy and stability in predicting financial risks, with a recognition accuracy rate of 100% for “ST” companies. This can significantly improve the early warning and management efficiency of financial risks for agricultural enterprises.