Identification and Guidance of Intimate Relationships Among Students Based on Reinforcement Learning

Authors

  • Guang Du Student Support Services, Zhejiang International Studies University, Hangzhou 310023, Zhejiang, China
  • Xiao Ju Liaoning Normal University, Dalian 116029, Liaoning, China
  • Yumo Ding Liaoning Normal University, Dalian 116029, Liaoning, China

Abstract

With regard to digital education management, the massive amounts of student behavioral data contain complex relationship information. Importantly, the intimate relationships of students have a significant impact on their mental health and the school’s education and teaching environment. Traditional student relationship management methods and existing analysis technologies have limitations in processing complex data and guiding student relationships. This study proposes an intelligent fusion network (IFN) model, which consists of a feature extractor, an information integrator, and a decision generator. By harnessing the collaborative operation of multiple components, it analyzes student relationship data. Experiments on a public campus social relationship dataset show that the IFN model has an 80% relationship recognition accuracy and scores 85% for the effectiveness
of guidance strategy, which is a significant improvement compared with the social network analysis model (recognition accuracy 40%, guidance strategy effectiveness 40%) and the support vector machine model (recognition accuracy 50%, guidance strategy effectiveness 50%). The research results confirm that the IFN model can effectively mine students’ intimate relationship information and provide reasonable guidance strategies. This study provides scientific and accurate decision support for school education management, enriches the theoretical content of educational technology in relationship analysis and intervention, and also provides a reference for subsequent research on the integration of technology and educational concepts.

Keywords: student intimacy, intelligent fusion network, relationship identification, guidance strategy, educational technology

Cite As

G. Du, X. Ju, Y. Ding, "Identification and Guidance of Intimate Relationships Among Students Based on
Reinforcement Learning", Engineering Intelligent Systems, vol. 34 no. 1, pp. 129-138, 2026.

 

 

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Published

2026-01-01

Issue

Vol. 34 No. 1 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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