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

This study introduces a tailored financial risk early warning model for agricultural enterprises, developed using the Long Short-Term Memory (LSTM) neural network algorithm. The model was constructed following an in-depth analysis of key financial risk indicators, leading to the creation of highly relevant feature samples. Through the LSTM network's training mechanism, which includes forget gates and memory cells, the model conducted forgetting and filtering operations on historical financial data. This process ensured the retention of key information for use in successive training cycles. The model's network weights and thresholds were optimized using the backpropagation algorithm, yielding a stable financial risk early warning model. The experimental results demonstrated the model's effectiveness in predicting key financial indicators and in accurately setting financial risk warning thresholds. The proposed LSTM model outperformed commonly used algorithms in terms of warning accuracy.