A Price Risk Early Warning Model for Agricultural Products Based on the Integration of Deep Learning and Knowledge Graphs

Agricultural price volatility poses significant challenges for food security, market regulation, and rural economic stability. To address these issues, this study proposes an integrated early warning model that combines the representational capabilities of deep learning with the interpretability of symbolic knowledge. The framework features two primary components: AgroFormer, a Transformer-based architecture specifically designed to capture hierarchical spatio-temporal dependencies across crops, regions, environmental factors, and time; and AgroScope, a strategic reasoning module that converts predictive insights into operational decisions through knowledge-aware constraints and risk-sensitive optimization. The model is evaluated on four benchmark datasets—Agmarknet, AGRIS, WorldCereal, and GAEZ—spanning structured tabular records, multilingual textual documents, and high-resolution remote sensing data. Experimental results show that the proposed approach achieves superior performance across all datasets, outperforming state-of-the-art baselines with an average improvement of 12.3% in prediction accuracy and a 12.1% reduction in RMSE. Ablation studies further validate the unique contributions of symbolic attribute alignment, differentiable scheduling graphs, and knowledge-aware constraints. Beyond technical contributions, the proposed system offers tangible value to agricultural practitioners. It enables proactive decision-making by delivering early warnings for price fluctuations, optimizing crop rotation schedules under policy and environmental constraints, and supporting adaptive planning in response to real-time market signals. The framework’s modular design facilitates deployment in diverse agro-ecological contexts, making it suitable for applications ranging from farm-level management to national food security planning. These results highlight the potential of combining deep learning with domain knowledge to create interpretable, scalable, and actionable tools for intelligent agricultural risk management.