Classification Method of Higher Vocational Education Students Based on K-Means Clustering Algorithm

It is the fundamental guarantee for formulating effective reform to classify higher vocational education students at the micro level. Firstly, a classification index system is established, which includes six primary indicators: general ideological literacy, basic professional abilities, core professional abilities, expansion professional abilities, comprehensive practical abilities, and continuous learning abilities. Each primary indicator is further subdivided into several secondary indicators. Secondly, the Analytic Hierarchy Process is used to determine the weight of primary indicators, while the weight of the secondary indicators is determined by the proportion of credits. Then, a weighted classification method based on K-Means clustering is designed, which is conducted on 261 students. The research results show that 261 students are divided into 5 types. The characteristics of the 5 types are discussed. Suggestions including clarifying the differential training path, strengthening technical skill teaching and strengthening career development education are put forward.