Title: K-means on application of means clustering in innovation and entrepreneurship sustainability education in universities

Authors: Weiyan Chen; Weibo Zheng

Addresses: School of Digital Creativity, Changzhou College of Information Technology, Changzhou, 213016, China ' Information Security Research Center, NARI Information and Communication Technology Co., Ltd., Nanjing, 210018, China

Abstract: Entrepreneurship education can help improve national competitiveness, and how to achieve sustainable development of entrepreneurship is a difficult problem to be solved. So this study proposes an improved K-means cluster analysis method based on literature and use data analysis to study the sustainable development of entrepreneurship. The improved K-means clustering method is more effective and efficient, with better clustering effect. By using the algorithm to analyse the influence of sustainable institutional environment on college students' entrepreneurship, when considering different institutions, the proportions of those who are willing to choose entrepreneurship are 68.1%, 87.5%, 65.5%, 80.3% and 89.6% respectively. Using the K-means algorithm can accurately reflect the situation of each student, and can grasp the learning needs of different students based on the obtained results. It provides rich and targeted educational resources for each student, providing a good development direction for personalised teaching methods in entrepreneurship education.

Keywords: k-means clustering; innovation; entrepreneurship; sustainable development; education.

DOI: 10.1504/IJCSYSE.2025.149222

International Journal of Computational Systems Engineering, 2025 Vol.9 No.2/3/4, pp.81 - 90

Received: 13 Apr 2023
Accepted: 08 Jun 2023
Published online: 20 Oct 2025 *

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