摘要
为深入了解学生的就餐需求,提升食堂的运营效率和服务质量,通过大数据特征挖掘分析技术,设计了高校食堂学生就餐行为分析方法.首先,通过Jinshuju.net平台收集学生就餐数据,利用大数据分析和数据挖掘技术,深入了解了高校学生的就餐行为、偏好和需求,并利用iiMedia工具判断就餐频率与就餐行为关系.经分析发现:学生的食堂就餐行为与餐费、食堂距离、等餐时间、寻坐时间、菜品品质、菜品品种等均存在相关性,但不同因素的相关程度不同.根据分析结果,可以制定有效的就餐优化方案,提升学生就餐水准.
Abstract
In order to gain a deeper understanding of students'dining needs,improve the operational efficiency and service quality of canteens,this study designed a method for analyzing student dining behavior in university canteens through big data fea-ture mining analysis technology.Firstly,this study collected student dining data through the Jinshuju.net platform,utilized big data analysis and data mining techniques to gain a deeper understanding of the dining behavior,preferences,and needs of college stu-dents,and used iiMedia tools to determine the relationship between dining frequency and dining behavior.After analysis,it was found that there is a correlation between student dining behavior in the cafeteria and meal costs,cafeteria distance,waiting time,sit-ting time,dish quality,dish variety,etc.,but the degree of correlation varies among different factors.Based on the analysis results,effective dining optimization plans can be developed to improve the dining standards of students.
万方数据