With the advancement of digital transformation, online courses have a profound impact on higher education. This study explores the impact of online course construction in higher education on the transformation of students’ learning styles, aims to analyze the current situation of online course construction and its value, proposes a course recommendation model based on knowledge graph, and explores its role in the transformation of students’ learning styles. The study combed the development history and construction content of online courses through the literature analysis method, constructed a knowledge graph graph convolutional network recommendation model (KGCN-CNSH) integrating common neighbors and structural holes, and conducted learning situation analysis based on 149,561 valid questionnaire data from 32 colleges and universities in 15 provinces. The results show that the KGCN-CNSH model improves the two metrics of Recall and NDCG by 0.83% and 0.39%, respectively, compared to the previous best-performing SGL algorithm on the Last-FM dataset; 75% of college students participated in at least one online course and took an average of 4.107; and the recommendation of online courses is the main factor influencing students’ gains in online learning one of them (p<0.001). The study conclusions show that online courses provide students with greater autonomy of choice while breaking through time and space constraints; the course recommendation model based on knowledge graph can effectively improve the level of learning personalization; and students' maintenance of concentration and persistence in the process of selfselecting courses is a key factor influencing the effectiveness of online learning.
