【摘要】 本研究旨在基于大数据技术，对大学生的行为模型进行分析和研究。通过收集包括一卡通消费、宿舍门禁、选课记录、社交媒体活动等多种数据，以及其他可能影响学生行为的因素，如图书借阅、网络登陆等，构建大学生行为相关模型。研究采用统计学、机器学习和数据挖掘等方法对数据进行处理和分析，探索大学生行为中的关键因素和潜在趋势。通过预测模型的建立，可以预测学生的孤僻度、亲密度、疑似贫困生等重要指标，为学校和教师提供更科学有效的教学指导和个性化辅导。更多还原

【Abstract】 The purpose of this study is to analyze and study the behavior model of college students based on Big data technology.By collecting various data such as e-card consumption, dormitory access control, course selection records, social media activities, and other factors that may affect student behavior, such as book borrowing and online login, a model related to college student behavior is constructed.The study uses methods such as statistics, machine learning, and data mining to process and analyze data, exploring key factors and potential trends in college student behavior.By establishing a predictive model, important indicators such as students′ loneliness, intimacy, and suspected poverty can be predicted, providing schools and teachers with more scientific and effective teaching guidance and personalized guidance.更多还原