Abstract: This study introduces a novel method for identifying and forecasting commodity price risks through the construction of dynamic causal knowledge graphs. Focusing on products traded on the Dalian Commodity Exchange, the proposed method models upstream and downstream enterprises along the industrial chain as "nodes", with 17 types of inter-enterprise relationships (e.g., industrial linkage, equity investment, capital flow) represented as "edges". The graph integrates relationship types including industrial chain connections, subsidiary affiliations, and equity holdings, and incorporates 30 attributes for each enterprise—such as business scope, geographic location, and registered capital—to build a causally enriched knowledge and event graph. Through causal identification, including Structural Causal Models (SCMs) and counterfactual analysis, the study derives three key risk indicators: (1) the industrial and regional distribution of relevant commodities (for revealing the spatial clustering and inter-regional economic dependencies of key enterprises); (2) the status of core enterprises (for identifying central nodes and their capacities for risk transmission within the network); and (3) the contagion paths of price risk (for quantifying such causal chains as "geopolitical conflict-supply chain disruption-price volatility", etc.). Enterprise risk levels are comprehensively evaluated using both positive and negative indicators, enabling real-time risk alerts. This approach addresses the limitations of traditional correlation—based statistical models, mitigates spurious associations in high-dimensional data, and significantly enhances early-warning accuracy. In the context of increasing global supply chain uncertainties, the method offers a data-driven, intelligent solution for safeguarding national strategic resources and improving enterprise risk management, in alignment with the 14th Five-Year Plan's emphasis on strengthening economic security and early warning systems.