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Intelligence customs declaration for cross-border e-commerce based on the multi-modal model and the optimal window mechanism

Author

Listed:
  • Xiaofeng Li

    (Nanjing University of Aeronautics and Astronautics)

  • Jing Ma

    (Nanjing University of Aeronautics and Astronautics)

  • Shan Li

    (Nanjing University of Aeronautics and Astronautics)

Abstract

This paper aims to study the intelligent customs declaration of cross-border e-commerce commodities from algorithm design and implementation. The difficulty of this issue is the recognition of commodity names, materials, and processing processes. Because the process of recognizing these three kinds of commodity information is similar, this paper chooses to identify the commodity name as the experimental research object. The algorithm in this paper is based on the premise of pre-clustering, using an optimal window mechanism to obtain the best word embedding vector representation. The Vision Transformer model extracts image features instead of traditional CNN models, and then text features are fused with image features to generate a multi-modal semantically feature vector. Finally, a deep forest classifier replaces the conventional neural network classifiers to complete the commodity name recognition task. The experimental results show that, for more than 600 different commodities on the 120,000 data records, the precision is 0.85, the recall is 0.87, and the F $$_1$$ 1 _score is 0.86. So, our algorithm can effectively and accurately recognize e-commerce commodity names and provide a new perspective on the research of e-commerce intelligence declarations.

Suggested Citation

  • Xiaofeng Li & Jing Ma & Shan Li, 2025. "Intelligence customs declaration for cross-border e-commerce based on the multi-modal model and the optimal window mechanism," Annals of Operations Research, Springer, vol. 348(1), pages 3-27, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-022-04799-w
    DOI: 10.1007/s10479-022-04799-w
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    References listed on IDEAS

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    1. César Alfaro & Javier Cano-Montero & Javier Gómez & Javier Moguerza & Felipe Ortega, 2016. "A multi-stage method for content classification and opinion mining on weblog comments," Annals of Operations Research, Springer, vol. 236(1), pages 197-213, January.
    2. Yongcong Luo & Jing Ma & Chi Li, 2020. "Entity name recognition of cross-border e-commerce commodity titles based on TWs-LSTM," Electronic Commerce Research, Springer, vol. 20(2), pages 405-426, June.
    3. César Alfaro & Javier Cano-Montero & Javier Gómez & Javier M. Moguerza & Felipe Ortega, 2016. "A multi-stage method for content classification and opinion mining on weblog comments," Annals of Operations Research, Springer, vol. 236(1), pages 197-213, January.
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