苏州科技大学学报（自然科学版）2025，Vol.42Issue(3)：78-84,7.DOI:10.12084/j.issn.2096-3289.2025.03.010
基于改进CS与大数据优化聚类的高校学生行为识别分析
Behavior recognition and analysis of college students based on improved CS and big data optimization clustering
摘要
Abstract
The application of big data in the field of education is becoming more and more extensive.At present,there are still some problems in the data processing effect and model generalization ability of student behavior recognition and analysis model.In view of this,this paper proposes an analysis model of college students' behav-ior recognition based on improved cuckoo search algorithm and fuzzy C-means clustering.The experimental re-sults show that the average F1 fraction of the proposed model is 0.982 6,and the average geometric mean is 0.902 3,which is superior to other models.Secondly,the AUC value of the model reaches up to 0.952.In terms of clus-tering effect,the sum of squares of clustering error of the model is the lowest 146,and the distribution of contour coefficients is concentrated between 0.8～0.9,and the scatter distribution is the most concentrated.In addition,in the analysis of students' social relations with different feature dimensions,the F1 value,recall rate and accuracy rate all exceed 0.90,showing good generalization ability and practicability.The research provides more accurate data support and decision-making basis for higher education management and student development assessment,and promotes the in-depth application of big data technology in the field of education.关键词
大数据/学生行为识别分析/布谷鸟搜索算法/高校教育/模糊C均值聚类Key words
big data/student behavior recognition and analysis/Cuckoo search algorithm/higher education/fuzzy C-means clustering分类
信息技术与安全科学引用本文复制引用
刘光宗,赵晓峰,张珍,刘桐瑞..基于改进CS与大数据优化聚类的高校学生行为识别分析[J].苏州科技大学学报（自然科学版）,2025,42(3):78-84,7.基金项目
国家社会科学基金项目(16BTQ085) （16BTQ085）
中国高校产学研创新基金课题(2022MU055) （2022MU055）
