IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/4211616.html
   My bibliography  Save this article

Research on Intelligent Customization of Cross-Border E-Commerce Based on Deep Learning

Author

Listed:
  • Chunrong Guo
  • Xiaodong Zhang
  • O.S. Albahri

Abstract

Cross-border e-commerce has become an important way of “New Infrastructure for Foreign Trade†and “Online Silk Road construction.†Utilizing intelligent technology to energize cross-border e-commerce can improve the quality and efficiency of the whole industrial chain. Cross-border e-commerce enterprises generally face the problem of product customization and development due to the large difference in international market demand. This study first analyzes the research status and technical underpinnings of intelligent customization of cross-border e-commerce, then establishes the technical framework of intelligent customization of cross-border e-commerce based on in-depth learning, and subsequently trains, tests, and analyzes the model, and the intelligent customization model has achieved a higher learning level. Finally, it puts forward the promotion and application strategy of cross-border e-commerce intelligent customization based on deep learning. It has theoretical significance and practical value for intelligently identifying changes in international market demand, helping cross-border e-commerce enterprises select products, and optimizing cross-border e-commerce product development.

Suggested Citation

  • Chunrong Guo & Xiaodong Zhang & O.S. Albahri, 2022. "Research on Intelligent Customization of Cross-Border E-Commerce Based on Deep Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, November.
  • Handle: RePEc:hin:jnlmpe:4211616
    DOI: 10.1155/2022/4211616
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/4211616.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/mpe/2022/4211616.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/4211616?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:4211616. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.