Financial Risk Prediction for Agricultural Enterprises Using Intelligent Modeling and Dynamic State Analysis

Agricultural enterprises have financial uncertainties due to market volatility, climate disruptions, and changes in policies; therefore, farming operations must use timely and accurate forecasts, as they are particularly vulnerable to external economic shocks and environmental variability. Standard forecasting methods usually cannot capture nonlinear dependencies and dynamic shifts in risk profiles; therefore, there is a need to consider intelligent, adaptive systems. Research proposes a novel financial risk prediction model using the Sooty Tern Optimization Algorithm Attention-Based Long Short-Term Memory (STOA-Att-LSTM). Financial risk data were collected, which included agricultural enterprise financial records, national weather databases, and commodity market indices. To ensure data integrity and modelling efficiency, two essential pre-processing techniques were employed. Handling missing values was performed using linear interpolation to reconstruct incomplete sequences, particularly in time-series financial and climatic data, to standardize variables, facilitating efficient model training and convergence. The STOA algorithm was used to optimize the hyper-parameters of the Att-LSTM model, enhancing its generalization and predictive accuracy. The attention mechanism enabled the model to dynamically focus on critical time-dependent features influencing financial risk. Dynamic state analysis further strengthened the framework by capturing temporal shifts in enterprise conditions. Model evaluation using Python-based implementation of error metrics and classification accuracy (0.9899) showed better results compared to traditional and baseline deep learning (DL) models. The proposed framework offers a robust, adaptive tool for proactive financial risk assessment in agricultural enterprises, supporting sustainable decision-making in uncertain environments.