机器学习在跨境电商海关数据评价中的应用研究

Research on the Application of Machine Learning in Customs Data Evaluation in Cross-Border E-Commerce

摘要: 海关数据评价是跨境贸易监管体系中的关键环节，其核心在于从高维、多源及异构的申报数据中识别潜在风险。随着跨境电商等新型贸易业态的快速发展，传统基于人工经验与规则库的方法在处理复杂数据时存在着效率低下与适应性不足的问题。机器学习作为人工智能的重要分支，依托统计建模与数据驱动机制，为海关数据评价提供了新的技术路径。本文基于机器学习与统计学交叉融合的视角，以跨境电商海关风险预警为应用场景，构建“专家评分–机器学习预测”的混合评价方法。通过引入岭回归与多输出回归模型，实现海关数据定性指标的定量化处理，完成14项定性风险指标的自动化预测。研究结果表明，该方法在提升海关数据处理效率与评价结果一致性方面具有一定优势，可为海关风险识别与智能监管提供切实的方法支撑。

Abstract: Customs data evaluation is a crucial link in the cross-border trade supervision system, its core being the identification of potential risks from high-dimensional, multi-source, and heterogeneous declaration data. With the rapid development of new trade formats such as cross-border e-commerce, traditional methods based on human experience and rule bases suffer from inefficiency and insufficient adaptability when processing complex data. Machine learning, as an important branch of artificial intelligence, provides a new technical path for customs data evaluation by relying on statistical modeling and data-driven mechanisms. This paper, based on the cross-border integration of machine learning and statistics, takes cross-border e-commerce customs risk early warning as an application scenario and constructs a hybrid evaluation method of “expert scoring-machine learning prediction”. By introducing ridge regression and multi-output regression models, the qualitative indicators of customs data are quantified, and the automated prediction of 14 qualitative risk indicators is achieved. The results show that this method has certain advantages in improving the efficiency of customs data processing and the consistency of evaluation results, and can provide practical methodological support for customs risk identification and intelligent supervision.