Construction and Implementation of Hierarchical Teaching Model based on Fast Clustering Algorithm

In view of the individual differences in student groups, the traditional teaching model cannot meet the needs of students’ all-round development. Stratified teaching focuses on individual differences and learning needs of students, and makes targeted teaching plans and objectives, which is conducive to the improvement of the overall teaching effect. By establishing the difference model in stratified teaching and taking the computer network course as an example, the hierarchical teaching model based on the fast-clustering algorithm (K-means) is designed to achieve the hierarchical learning of students. The simulation experiment shows that the hierarchical teaching model based on the fast-clustering algorithm is more scientific and reasonable than the hierarchical method of expert classification algorithm and student total score ranking, and can provide reference for other disciplines.