Application and Research of Attention Mechanism Combined with Data Visualisation for Entrepreneurial Learning Course Recommendation System in Universities and Colleges

Abstract

With the rise of entrepreneurship boom, the number of entrepreneurship courses in colleges and universities is increasing. However, the traditional course recommendation system is often lacking in individuation and cannot adapt to the dynamic changes of students' needs. Therefore, the study proposes an innovative converged recommendation system that combines Attention Mechanism (AM) with Data Visualization (DV) techniques to enhance personalized recommendation capabilities for entrepreneurial learning courses. By analyzing students' interests and needs in real time, this method uses attention mechanism to dynamically adjust recommended content, while using data visualization technology to visually display course characteristics, so as to improve students' participation and learning effect. Extensive performance testing on the Enlec dataset showed that the fusion system significantly outperformed traditional methods in both recommendation accuracy and coverage, with an overall recommendation accuracy of 99.4 %. In the results of the recommendation test for 685 students, the highest course selection rates for the four systems were 74 %, 71 %, 68 % and 63 %, respectively, while the recommendation effectiveness of the integrated entrepreneurship course reached 98.5 %. The results confirm the effectiveness and robustness of the proposed method in practical application. The final results show that the proposed system not only improves the course selection rate of students, but also significantly enhances their interest in entrepreneurial learning courses, providing an effective solution for personalized learning in higher education.