Journal of Engineering, Project, and Production Management, 2026, 16(2), 2025-166
Evaluating Innovation and Entrepreneurship Education Using Neural Network Models
Lecturer, Law School of Weifang University, Weifang 261061, Shandong, China. E-mail: dcfgod17@163.com
Project Management
Received August 16, 2025; revised October 6, 2025; accepted October 7, 2025
Available online March 7, 2026
Abstract: This research evaluates the efficacy of innovation and entrepreneurship in higher education institutions and proposes an assessment approach that uses an enhanced multivariate neural network to improve the scientific rigor and precision. Existing evaluation methods are plagued by simplistic index systems, an inability to capture nonlinear relationships between indicators, and low adaptability to high-dimensional data. This leads to insufficient evaluation accuracy and poor practical guidance. First, we construct an assessment index system covering five aspects: student’s entrepreneurial achievements, innovation ability, curriculum teaching, teacher construction, and practice platform. We then optimized the Multivariate Neural Network (MNN) model using the Heap-Based Optimizer (HBO) and experimentally verified on the basis of 15,328 data points from 56 “dual-innovation” demonstration institutions between 2015 to 2022. The results show that the HBO-MNN model outperforms the traditional methods, achieving an accuracy of 0.954 precision of 0.9322, recall of 0.941, and an F1 score 0.9585 and the evaluation time is only 0.801 seconds. Feature importance analysis revealed that the number of cooperative platforms, the size, the practice bases and course examination results had the greatest impact on the assessment results. The study demonstrates that this method can effectively improve assessment accuracy and provide data support for universities to optimize innovation and entrepreneurship programs. In the future, multimodal data can be integrated to further improve the method’s applicability.
Keywords: Multivariate neural network algorithm, evaluation of the effectiveness, innovation and entrepreneurship education, development trend analysis, intelligent optimization.
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Citation: Yu, L. (2026). Evaluating Innovation and Entrepreneurship Education Using Neural Network Models. Journal of Engineering, Project, and Production Management, 16(2), 2025-166.
