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<p>This study is dedicated to exploring the construction and implementation of innovation and entrepreneurship education system in higher vocational colleges. By analyzing the relationship between education investment and industrial employment, it further enhances the innovation and entrepreneurship ability of students in higher vocational colleges. It promotes the synergistic development of education and economy. First, the relationship between higher education investment and industrial employment is analyzed by VAR model, which reveals the influence of education on the structural adjustment of industrial jobs. Second, the evaluation indexes and quality clustering of innovation and entrepreneurship education are refined through fuzzy cluster analysis and K-means clustering method based on seagull optimization algorithm. The research results show that the improved innovation and entrepreneurship education system can significantly improve students’ innovation and entrepreneurship satisfaction and self-evaluation, in which the indicators of entrepreneurial spirit and psychological quality are considerably enhanced. In addition, students’ innovation and entrepreneurship ability showed significant differences in multiple dimensions. This study confirms the effectiveness of the construction of innovation and entrepreneurship education system in higher vocational colleges, which is substantial in improving students’ innovation and entrepreneurship ability.</p>
</abstract>

This study is dedicated to exploring the construction and implementation of innovation and entrepreneurship education system in higher vocational colleges. By analyzing the relationship between education investment and industrial employment, it further enhances the innovation and entrepreneurship ability of students in higher vocational colleges. It promotes the synergistic development of education and economy. First, the relationship between higher education investment and industrial employment is analyzed by VAR model, which reveals the influence of education on the structural adjustment of industrial jobs. Second, the evaluation indexes and quality clustering of innovation and entrepreneurship education are refined through fuzzy cluster analysis and K-means clustering method based on seagull optimization algorithm. The research results show that the improved innovation and entrepreneurship education system can significantly improve students’ innovation and entrepreneurship satisfaction and self-evaluation, in which the indicators of entrepreneurial spirit and psychological quality are considerably enhanced. In addition, students’ innovation and entrepreneurship ability showed significant differences in multiple dimensions. This study confirms the effectiveness of the construction of innovation and entrepreneurship education system in higher vocational colleges, which is substantial in improving students’ innovation and entrepreneurship ability.

Construction and Implementation of Innovation and Entrepreneurship Education System in Higher Vocational Colleges in the New Era Based on VAR Modeling

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study is dedicated to exploring the construction and implementation of innovation and entrepreneurship education system in higher vocational colleges. By...

Construction and Implementation of Innovation and Entrepreneurship Education System in Higher Vocational Colleges in the New Era Based on VAR Modeling

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 15 gen 2024

Accettato: 22 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0631

Parole chiave
Fuzzy cluster analysis, Seagull optimization algorithm, K-means clustering, Innovation and entrepreneurship

© 2023 Changjiang Xu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction and Implementation of Innovation and Entrepreneurship Education System in Higher Vocational Colleges in the New Era Based on VAR Modeling

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 15 gen 2024

Accettato: 22 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0631

Parole chiaveFuzzy cluster analysis, Seagull optimization algorithm, K-means clustering, Innovation and entrepreneurship

© 2023 Changjiang Xu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Fuzzy cluster analysis, Seagull optimization algorithm, K-means clustering, Innovation and entrepreneurship