随着校园的信息化程度越来越高，校园卡在学校中的使用已经普遍存在，但是学校在校园卡的数据利用方面，由于校园卡交易的数据量庞大，学校对校园卡管理成效并不显著。本文通过对校园卡数据的统计分析以及数据挖掘系统的研究及实现，针对校园卡的消费行为及校园卡消费数据对贫困生的发掘进行了研究。本文的研究结果，对于学校的数据管理和决策具有重要意义，可以作为学校判定贫困生的依据条件之一。 我国拥有庞大的校园卡市场，并且工作终端布局分散，因此建立以数据挖掘为基础的消费行为分析系统。研究针对数据挖掘模型通过对学生消费流水的分类以及建立关联规则进行分析，主要是在支持向量机方法与关联规则的理论基础上对消费方式的运用展开论述，通过对分类样本按照关联规则进行优化，针对校园学生的消费流水，通过对校园卡消费数据的准备、筛选及清洗获得可用的数据，并建立其匹配的分析环境。首先对校园卡消费行为进行分析，参考和统筹相关的标准，通过对关联规则分析对数据进行分类，然后针对系统结构和功能模块进行实现，最后将校园卡的消费数据，按照3个维度分别是系部、专业、生源地等进行划分，对源数据进行预处理、对支持向量机进行分类和对所获得的数据进行挖掘，并获得贫困生的分析结果。 论文对于消费卡的消费记录进行数据分析，无论在理论应用和其实践方面都具有积极的意义，论文也从技术角度论证了数据挖掘对于贫困生资助对象的，选取和资助方案的制定是可行的，进行数据细化并将相关的项目集进一步的完善，在未来的应用中必将得到较大的推广，在一定程度上将高校贫困生管理工作简单化、系统化。

With increasing degree of information technology campus high school campus card use more and more, but in terms of data utilization school campus card, because of the huge amount of data campus card transaction, the school campus card management performance is not significant. This study and implementation of campus card data, statistical analysis and data mining system through in-depth study of campus card consumer behavior, and according to the campus card spending data, poor students excavations were studied. Results of this study, data for school management and decision-making is important, can be used as the basis for one school students in disadvantaged determination. China has a huge campus card market, and the working terminal scattered layout, thus establishing data mining based consumer behavior analysis system. Research on data mining model by student water consumption classification and association rules analysis, mainly in support vector machine, and the use of the theoretical basis of association rules on the consumption mode of expansion discussed by the classification of samples in accordance with the association rules optimization, mainly for students on campus water consumption by preparing for campus card consumption data, screening and cleaning, for available data and establish its match analysis environment. First campus card consumer behavior analysis, reference and co-ordinate the relevant standards, by association rules to classify data, and then implement the structure and function modules for the system. Finally, the campus card consumption data, according to three dimensions are, departments, professional, and other students to be divided, the source data preprocessing for SVM classification and data obtained mining, and get analysis of the results of the poor students. Papers for consumption record consumer cards for data analysis, has a positive meaning both in theory and its practice, the paper also from a technical point of view demonstrated data mining for poor students object, select and develop support programs are feasible , refining and related data collection projects further improvement in future applications will get a larger promotion, to a certain extent poor College students simplify management, systematic.