Title: Research on innovation and entrepreneurship knowledge management in higher vocational colleges using big data analysis

Authors: Xiaoyue Xu

Addresses: Student Affairs Office, Jiangsu College of Engineering and Technology, NanTong, 226001, China

Abstract: In response to the current lack of a comprehensive innovation and entrepreneurship education system, and intelligent evaluation methods in many universities, this study designed a course big data analysis model based on k-means clustering and FP growth algorithm to obtain the degree of correlation between different courses and innovation and entrepreneurship practices. The research results show that the combined algorithm spends less time in mining a large amount of data than apriori derived association rule mining algorithm, FP tree* algorithm and MDML-GA algorithm. And the FP-growth algorithm mining course data found that the results of innovation and entrepreneurship practice are highly correlated with the results of basic theory, with a confidence of 0.91. Therefore, the algorithm proposed in the study has advantages in analysing the influencing factors of students' innovative thinking and entrepreneurial ability, and is also of great significance in promoting the reform of teaching methods.

Keywords: educational model; FP growth algorithm; higher education; innovation and entrepreneurship; K-means clustering algorithm.

DOI: 10.1504/IJCSYSE.2025.145000

International Journal of Computational Systems Engineering, 2025 Vol.9 No.1, pp.48 - 57

Received: 23 Apr 2023
Accepted: 11 Sep 2023
Published online: 17 Mar 2025 *