Students in adolescence are not mature in mind, thought, ability and other aspects, which are easily affected by various emotional behaviors. Positive emotional behavior contributes to students’ mental health and academic progress. Negative emotional behavior would lead to psychological problems and academic frustration. If it is not paid attention to, students may act out of control under the control of negative emotions, thus resulting in serious mental illness, which is not conducive to the education and management of students. Due to the rapid development of social information network and science and technology, the analysis of students’ emotional behavior and educational management by pure human intervention has fallen behind, and it is impossible to timely feedback, track and predict students’ status. This paper introduced the general direction and achievements of human-computer interaction research, and discussed the combination of big data and human-computer interaction. The method of applying human-computer interaction technology to students’ emotional behavior analysis and education management was studied. The pure human intervention method was compared with facial emotion recognition, voice emotion recognition, human-computer body feeling interaction and virtual scene education methods under human-computer interaction technology. Five experimental groups were designed to conduct research in three aspects of emotional behavior analysis, education and learning, and supervision and management. It was found that the average accuracy of facial emotion recognition for emotional behavior analysis was 88.0%; the average course learning efficiency of virtual scene education used for students’ educational learning was 82.8%, and the total progress was up to 99.81%; the average success rate of human-computer somatosensory interaction for supervision and management was the highest, which was 68.1%.
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