Sustainable supply chain risk prediction based on a meta-heuristic algorithm in cross-border e-commerce

Cross-border e-commerce has expanded rapidly in recent years, increasing the complexity of sustainable supply chain management (SSCM) and amplifying operational risks that are difficult to assess using traditional approaches. This article investigates sustainable supply chain (SSC) risk prediction in cross-border e-commerce and proposes a hybrid, data-driven assessment framework that integrates expert knowledge with machine learning. First, qualitative and multi-source risk indicators are structured and quantified using the Analytic Hierarchy Process (AHP) to obtain comparable numerical representations. Second, a support vector machine (SVM) model is constructed for risk prediction, and its key hyperparameters are optimized using meta-heuristic search to enhance predictive performance. Experimental results based on the enterprise dataset indicate that the proposed genetic algorithm-particle swarm optimization-support vector machine (GA-PSO-SVM) approach outperforms all benchmark methods, achieving an approximately 20% reduction in root mean square error (RMSE) compared with the non-optimized baseline. Overall, the proposed method offers an interpretable and practically applicable framework for intelligent SSCM risk assessment in cross-border e-commerce, thereby providing methodological support for risk monitoring and decision-making in sustainable supply chains.