Abstract:Based on the existing data mining technology,this article adopts the method of optimizing the initial clustering center to improve the k-means clustering algorithm, we can study of Xinjiang agricultural University student id card consumption data for the research and analysis,and provide decision support for the related departments.First of all, according to the demand analysis, we will choose some students for school year 2014-2015 real data in one cartoon system as data analysis,and data preprocessing, at the same time, we will choose the dining room number and amount, the supermarket consumption number and amount, the dining place for experimental characteristic attributes;Secondly, we use the improved clustering algorithm to analyze the data, and comparative analysis based on three kinds of distance measure under the k-means clustering algorithm;Then, the analysis conclusion, the student canteen consumption behavior and supermarket consumption behavior;Finally,the study was based on the conclusions of analysis provides decision support for schools.