IDEAS home Printed from https://ideas.repec.org/a/igg/jitn00/v17y2025i1p1-23.html
   My bibliography  Save this article

Research on Intelligent Optimization and CAD Innovative Design of Interior Space Layout Aided by Neural Network

Author

Listed:
  • Yi Li

    (Kookmin University, South Korea)

  • Zhuang Miao

    (Kookmin University, South Korea)

  • Xiaotong Jiang

    (Kookmin University, South Korea)

  • Guanxi Chen

    (NingboTech University, China)

Abstract

With the rapid development of artificial intelligence and computer-aided tools, the field of interior design is also undergoing new evolution, and intelligent software has promoted the improvement of efficiency and creativity in interior space design. The decisions of various optimization systems also contribute to the development of indoor spatial layout towards new intelligent distribution. This article uses neural network deep learning algorithms and CAD computer-aided tools to study intelligent optimization and innovative design of indoor spatial layout. Extracting spatial layout feature information of indoor buildings using neural network algorithms. Utilizing supervised learning estimation models of neural networks for data training to achieve intelligent optimization of spatial practicality. A collaborative drawing design method was proposed using CAD computer-aided design. The research results indicate that neural networks and CAD assisted tools also have good innovative design effects in drawing 3D models of indoor spaces.

Suggested Citation

  • Yi Li & Zhuang Miao & Xiaotong Jiang & Guanxi Chen, 2025. "Research on Intelligent Optimization and CAD Innovative Design of Interior Space Layout Aided by Neural Network," International Journal of Interdisciplinary Telecommunications and Networking (IJITN), IGI Global Scientific Publishing, vol. 17(1), pages 1-23, January.
    • Handle: RePEc:igg:jitn00:v:17:y:2025:i:1:p:1-23
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJITN.386144
    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:jitn00:v:17:y:2025:i:1:p:1-23. 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.