IDEAS home Printed from https://ideas.repec.org/a/igg/jdst00/v16y2025i1p1-17.html
   My bibliography  Save this article

Construction of a Research Model for Overseas Dissemination of Chinese Literature Based on Deep Learning Models

Author

Listed:
  • Hongjuan Zhang

    (Shanghai Zhongqiao Vocational and Technical University, China)

Abstract

With the comprehensive and in-depth implementation of the “going global” strategy of Chinese culture, research on the overseas dissemination of Chinese literature has gradually become a prominent discipline. This article first defines the essence of overseas translation of Chinese literature, and based on Brado's 7W communication model, combined with the current situation of overseas dissemination of Chinese literature, analyzes the motivation of translation ecology research on overseas dissemination of Chinese literature. Through simulation experiments and actual data analysis, the differences in the accuracy of individual and joint regularization methods in small sample high-dimensional feature selection of deep learning models were compared. The research on the overseas dissemination of Chinese literature is still in its infancy, and conducting research and analysis on it is of great significance.

Suggested Citation

  • Hongjuan Zhang, 2025. "Construction of a Research Model for Overseas Dissemination of Chinese Literature Based on Deep Learning Models," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 16(1), pages 1-17, January.
  • Handle: RePEc:igg:jdst00:v:16:y:2025:i:1:p:1-17
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDST.375389
    Download Restriction: no
    ---><---

    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:igg:jdst00:v:16:y:2025:i:1:p:1-17. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.