The rapid expansion of New Energy Vehicles (NEVs) has increased the global NEV supply chains' exposure to diverse and interconnected risks. Distributed production networks frequently face disruptions driven by raw material volatility, evolving environmental regulations, customs clearance uncertainty, and geopolitical instability, underscoring the need for effective early-warning systems. To address limitations in existing studies that lack a consistent and interpretable structure for NEV-specific hazards, this study proposes a hybrid NLP-based pipeline for risk text classification and early-warning sender extraction. A curated dataset of 120 NEV-related risk reports published between 2023 and 2025 was collected from Chinese information sources, pre-processed, and annotated according to a six-category risk taxonomy. Classical machine-learning models, including logistic regression, support vector machines, random forest, and XGBoost, were trained using TF-IDF features, while a multilayer perceptron and a BERT model were employed to capture nonlinear patterns and contextual semantics. Classical models were evaluated using five-fold cross-validation, and deep models were assessed on a held-out test set. XGBoost achieved the best classical performance, with accuracy and F1 scores of 0.826 and 0.766, respectively. BERT outperformed all baselines, reaching an accuracy of 0.864 and an F1 score of 0.808. The proposed framework demonstrates a modular and scalable approach.
Chaoke et al. (Thu,) studied this question.
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