Learning Path Recommendation Method Based on Feature Similarity and Jaccard Median

The advancement of the new college entrance examination has prompted more and more colleges to convert their enrollment mode from professional enrollment to enrollment in general categories.However,relevant studies indicate that there is a lack of rationality in students' choices when it comes to major shunts.How to break the situation of “cold majors and hot majors” caused by the imbalance of major selection has become the core problem faced by large types of training models.A learning path recommendation method based on feature similarity and Jaccard median(CFSJM) is proposed in this paper,aiming to provide direction navigation and learning path recommendations for students when choosing their majors.The method utilizes Node2vec to learn the interactions between students and knowledge points to obtain a feature representation of student nodes.A linear regression model is trained to predict the students' major direction,and a learning path candidate set is generated based on feature similarity,which in turn introduces the Jaccard median theory to generate learning paths.Experimental results show that the accuracy of CFSJM in the offline teaching data is better than that of the existing methods,which provides a new idea to give full play to the advantages of enrollment in general categories in cultivating innovative talents and improving the quality of university education.