Student Service Group Clustering and Community Model Construction Based on Data Mining Technology

In recent years, college students’ community service in China has received due attention and played a great role in the cultivation of students’ moral quality. But on the whole, there are some problems, such as high randomness, low level, not wide range of services, lack of universality and so on. In this regard, we can learn from the existing practices and experience of student community service and strengthen the research on student community service. College student community is a complex system integrating education, management and service. Systematicness is the basic feature of college student community management. Only by making full use of systematic and scientific ideas and methods can we seek the optimization of the overall benefits of student community system. Under the guidance of the system view, this paper tries to simulate and reproduce the operation mechanism and behavior mode of local college student community management from the global and dynamic point of view. Combining with relevant literature, it analyzes students’ behavior by using data mining technology. On the basis of in-depth analysis of the whole operation process of college student community, it constructs the model of college student community system, thinks systematically, explores ways to improve the overall operation efficiency of college student community, and then puts forward suggestions and ideas for constructing college student community mode.