摘要
在高校信息系统的快速发展背景下,海量学生数据的积累形成了独特的校园大数据环境,为提升高校信息管理水平提供了重要的数据基础。提出了一种基于改进AP聚类算法的大学生个人行为信息画像构建方法,旨在为高校信息管理提供更精准的分析工具。对学生原始行为数据进行标准化预处理,确保数据的规范性和可比性;通过引入自适应调整机制优化AP算法中的参考度参数,显著提升了聚类效果。在此基础上,利用相似度函数提取学生的行为特征,并结合学院归属、学号等基本信息,构建了全面且细致的个人行为画像。为高校信息管理提供了一种创新、高效的解决方案,具有重要的理论价值和实践意义。
Under the background of the rapid development of information systems in colleges and universities,the accumulation of massive student data has formed a unique campus big data environment,providing a critical foundation for improving information management in colleges and universities.This study proposes a method of constructing personal behavioral information portraits of college students based on an improved AP clustering algorithm,aiming to deliver more precise analytical tools for information management in colleges and universities.The approach involves to standardize preprocessing of the student′s original behavior data to ensure the standardization and comparability of the data;and optimize the reference parameter in the AP algorithm by introducing an adaptive adjustment mechanism to improve clustering performance.On this basis,the similarity function is used to extract students′behavioral characteristics,and a comprehensive and detailed personal behavior portrait is constructed by combining the basic information such as college affiliation and student ID.The method offers an innovative and efficient solution for information management in colleges and universities,which is of great significance both in theory and practice.
作者
朱丽丽
ZHU Lili(Jinling Institute of Technology,Nanjing 211169,China)
出处
《常州信息职业技术学院学报》
2025年第4期37-42,共6页
Journal of Changzhou College of Information Technology
基金
2024年金陵科技学院学生工作专项课题“‘三全育人’背景下‘一站式’学生社区建设研究”(JIT-XSGL-202402)
2024年江苏省高等教育学会课题“数智技术赋能高校思想政治工作精准化实践研究”。
关键词
高校大学生个人行为
AP聚类算法
画像分析技术
行为信息
personal behavior of college students
AP clustering algorithm
portrait analysis techniques
behavioral information
