摘  要

随着高校信息化建设的不断完善，在校大学生日常生活和学习行为被各大业务系统记录和存储下来，并且得到了持续的积累，初步形成了具有大规模，多类型学生个人大数据环境。数字化校园系统的广泛运用，数据的快速传递和使用，潜移默化的在改变高校学生消费行为。笔者将从数字化校园运用入手，来分析此系统的构建及对学生消费行为的影响。

高校学生是使用移动支付的庞大群体，移动支付方式也不可避免地给他们的消费行为带来了各种影响。本文从移动支付的定义和特点出发，分析了移动支付时代下高校学生的消费行为，并针对移动支付迅速发展情况下大学生消费行为的问题提出了建议。

大数据时代的到来，Python语言越来越凸显出它的优势。Python丰富的工具包让它在科学计算，文件处理，数据处理，数据可视化等领域越来越凸显其价值。所以本次设计分析模型采用Python实现，使用Python处理河池学院大学生消费信息，得到可视化图并通过django展示，从数据中发现学生的消费水平，消费习惯等隐藏的信息.通过对这些数据进行统计，分析，可以对大学生的消费有一个整体的把握，对具体的消费行为也有一个精准的判断，并给出对应建议。

关键词：学生消费；消费行为；分析系统；Python；django

ABSTRACT

With the continuous improvement of the information construction in colleges and universities, the daily life and learning behavior of college students are recorded and stored by various business systems, and have been continuously accumulated, which has initially formed a large scale. Multi-type student personal big data environment. The wide application of digital campus system and the rapid transmission and use of data change the consumption behavior of college students. The author will analyze the construction of this system and its influence on students' consumption behavior from the application of digital campus.

College students are a large group of mobile payment, and mobile payment inevitably has a variety of effects on their consumption behavior. Based on the definition and characteristics of mobile payment, this paper analyzes the consumption behavior of college students in the era of mobile payment, and puts forward some suggestions on the consumption behavior of college students under the rapid development of mobile payment.

Big data era, Python language more and more highlights its advantages. Python rich toolkit makes it more and more valuable in scientific computing, file processing, data processing, data visualization and so on. Therefore, the design and analysis model is realized by Python, using Python to process the consumption information of college students in Hechi College, obtaining visual map and displaying it through django, and finding hidden information such as students' consumption level, consumption habits and so on from the data. Through the statistics and analysis of these data, we can grasp the consumption of college students as a whole, have an accurate judgment on the specific consumption behavior, and give corresponding suggestions.

Keywords: student consumption; consumption behavior; analytical system; Python;django