ASP Cluster analysis of humanistic quality evaluation data for private college students using NET technology and K-Means algorithm

This study aims to explore how to utilize ASP NET technology and K-Means clustering algorithm are used to analyze the humanistic quality evaluation data of private university students. Firstly, by introducing ASP We have explained the basic principles and functions of NET, and its application in efficiently processing and managing large amounts of educational data. Next, the working principle of K-Means algorithm and its effectiveness in data clustering were introduced in detail. In the study, we constructed a humanistic quality evaluation system that includes multiple evaluation indicators, aiming to comprehensively evaluate the humanistic quality of students. By collecting relevant data on students from private universities and utilizing ASP NET is used for data preprocessing and management, and then K-Means algorithm is applied for data clustering analysis. The results revealed the distribution of different dimensions of humanistic literacy and identified key influencing factors, providing data support for further improving education quality and student humanistic literacy. In addition, this study also discussed the limitations of the method and made suggestions for future research directions. Through this study, we demonstrated ASP The practicality of NET technology and K-Means algorithm in educational data analysis provides a new perspective for private universities to evaluate and enhance students' humanistic qualities.