AHP-GGAGR Based Innovative and Entrepreneurial Learning Platform for University Students

Abstract

As the boost of the transformation of economy, innovation and entrepreneurship programs have become increasingly important in colleges and universities. Aiming at solving the problem that college students cannot obtain high-quality learning materials and related competition questions when they participate in innovation and entrepreneurship programs. The study firstly constructs the overall framework of the innovation and entrepreneurship platform for college students, and then constructs the cluster KAI model and the competition question model based on hierarchical analysis to portray the core disciplinary competence of college students and recommend the competition questions. Finally, a fusion gated graph-attention group competition topic recommendation model is proposed to complete the recommendation of competition topics and information such as materials by capturing the higher-order features of groups and competition topics. The results show that the research model starts to converge after 462 iterations and the convergence is stable. Meanwhile, when the recommendation list is 12, the model recommendation accuracy reaches 98.2%, and the AUC-ROC detection index is 0.865. It shows that the model constructed by the research has high accuracy and stability. The integration of the research model into the innovation and entrepreneurship learning platform for college students can greatly reduce the corresponding time of the platform, and the comprehensive satisfaction of college student users with the platform is above 85%. Through the platform constructed by the research, students can obtain relevant competition questions and learning resources more accurately, and the outcomes could also offer a theoretical basis for the construction of other platforms of the same type.

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