Cluster Analysis on Polytechnic Professional Settings and AI Work Based on Entropy Power Method
Authors
Jiangyi Lv1, Cherry Jiang2, Zhiwang Gan3, *
1Beijing Polytechnic University, Beijing, China
2Columbia University, New York, US
3Tianjin Peisheng Education Technology Co., Ltd, Tianjin, China
*Corresponding author.
Email: mhi2008@yeah.net
Corresponding Author
Zhiwang Gan
Available Online 25 August 2025.
- DOI
- 10.2991/978-2-38476-456-3_29How to use a DOI?
- Keywords
- Cluster Analysis; Polytechnic Professional Setting; AI (Artificial Intelligence); Entropy Power Method
- Abstract
Vocational education in undergraduate colleges and universities is an important part of the national modern education system. We aim for comprehensive literacy in training goals, reinforcing the integration of industry and education within professional contexts, and fostering organizational innovation in governance capabilities. Entropy Power method is a method that uses information entropy theory to calculate. This Entropy Power Model BigData Analysis and cluster Research on Vocational Education Professional Setting and Artificial Intelligence Career Orientation just Based on Entropy Power Method.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jiangyi Lv AU - Cherry Jiang AU - Zhiwang Gan PY - 2025 DA - 2025/08/25 TI - Cluster Analysis on Polytechnic Professional Settings and AI Work Based on Entropy Power Method BT - Proceedings of the 5th International Conference on New Computational Social Science (ICNCSS 2025) PB - Atlantis Press SP - 245 EP - 252 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-456-3_29 DO - 10.2991/978-2-38476-456-3_29 ID - Lv2025 ER -