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Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network

  • Guo LiEmail author
  • Na Li
Article
  • 3 Downloads

Abstract

Customs classification is an essential international procedure to import cross-border goods traded by various companies and individuals. Proper classification of such goods with high efficiency in light of the rapidly increasing amount of international trade is still challenging. The current abundant e-commence data and advanced machine learning techniques provide an opportunity for cross-border e-commerce sellers to classify goods efficiently. Thus, in this paper, we propose a text-image adaptive convolutional neural network to effectively utilize website information and facilitate the customs classification process. The proposed model includes two independent submodels: one for text and the other for image. The submodels are fused by a novel method, which can adjust the value of parameters according to the model training result. Finally, we conduct a case study and comparison experiments based on a group of customs tariff codes and a data set from an e-commerce website. Experiment results indicate the effectiveness of text and image combination in performance improvement, the outperformance of the adaptive fusion method, as well as the potential of this approach when applied to customs classification.

Keywords

Customs classification Cross-border e-commerce Convolutional neural network Text and image classification 

Notes

Acknowledgements

The authors sincerely thank the editors and anonymous reviewers for their insightful comments and suggestions. This research is partially supported by the National Natural Science Foundation of China under the grant nos. 91746110, 71372019, 71521002, 71642004; the Joint Development Program of Beijing Municipal Commission of Education.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Management and EconomicsBeijing Institute of TechnologyBeijingChina
  2. 2.Center for Energy and Environmental Policy ResearchBeijing Institute of TechnologyBeijingChina
  3. 3.Sustainable Development Research Institute for Economy and Society of BeijingBeijingChina

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