Extraction and analysis of individual features of students’ ideological and political education based on big data clustering algorithm

Big data clustering algorithm empowers ideological and political education, which can outline the total picture of students in an all-round way and reveal the characteristics and laws of the group. In this paper, the FCM algorithm is used to construct the student portrait, and the obtained data are clustered and analyzed. On the basis of the data generated by the school information system, three individual characteristics of students’ ideological and political education with certain universal significance are extracted, namely, diligence, sleep pattern and consumption behavior. The target group index TGI is introduced to characterize different students, and the average TGI value of the five types of student groups is 86.298 for diligence, 119.5 for sleep pattern, and 97.534 for consumption behavior. This paper has important guiding significance for colleges and universities to incorporate precise thinking and adjust ideological and political work strategies.