Social Relationship Mining Based on Student Data

In recent years, the campus social network mining has been widely concerned by scholars because it is closely related to the physical and mental health development of college students and the university management work. Compared with the typical social network mining tasks, the challenges of campus social network mining mainly lie in the difficulty of data acquisition and unlabeled data. Based on the phenomenon of homogeneity between friends, this paper proposed a social relationship mining method. Firstly, this paper carried out data dimension reduction operation for each student's daily consumption data, and then innovatively carried out the convolution operation for the consumption behavior data after dimension reduction, and finally obtained the characteristics of students' consumption behavior. The Multiple dimensional scaling algorithm is adopted for data dimension reduction, and the convolutional network is an improved Lenet-5 network. Then, the personalized ranking model is developed base on the phenomenon that the distance between friends is less than the distance between non-friends. To solve the problem of unlabeled data, we mined the friends' relationship from the student's library access control system swiping card records, and then taken the mined friends' relationship as the real friends' relationship to train the model. From the experimental results, the social network mining method proposed in this paper performs well.