Abstract
At present, the abnormal psychological recognition of middle school students is mainly through psychological questionnaire, after data statistical processing, to assess whether there is abnormal psychological students. The recognition accuracy is strongly dependent on the reliability of the questionnaire, which leads to the poor recognition accuracy and stability. In order to solve these problems, the method of abnormal psychological recognition of students in mobile PE teaching based on data mining will be studied. After analyzing the influence of PE teaching on students’ psychology, the behavioral characteristics that represent students’ psychology are extracted. After constructing the students’ psychological view, the students are classified preliminarily. Through constructing mental state mining decision tree, using iForest algorithm to realize abnormal mental recognition for middle school students. The test results of recognition method show that the accuracy of the mental recognition method is stable between 87.28% and 87.95%, and the recognition reliability is higher.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xiang, S., Lin, W., Liu, J.: A randomized controlled study of effect of group counseling on facial expression recognition in college students with social delay [J]. Chin. Ment. Health J. 35(03), 177–181 (2021)
Liu, D., Bao, L., Wan, C., et al.: Multi-layer partial information fusion model for psychological crisis identification of online forum users. J. Chin. Comput. Syst. 42(04), 690–699 (2021)
Chakladar, D.D., Dey, S., Roy, P.P., Dogra, D.P.: EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm. Biomed. Signal Process. Control 60, 101989 (2020)
Fei, Z., Yang, E., Li, D., et al.: Deep convolution network based emotion analysis towards mental health care. Neurocomputing 388, 212–227 (2020)
Su, Y., Liu, M., Zhao, N., et al.: Identifying psychological indexes based on social media data: a machine learning method. Adv. Psychol. Sci. 29(04), 571–585 (2021)
Shang, Y., Yang, N.: An improved particle swarm optimization back propogation neural network algorithm for psychological stress identification. Sci. Technol. Eng. 20(04), 1467–1472 (2020)
Zhao, T., Huang, Z., Wang, X., et al.: Recognition research on concealing behavior in psychological test. Comput. Eng. Appl. 56(20), 158–164 (2020)
Funding
Project of Shaanxi Provincial Department of Education: Research on the Intrinsic Mechanism of the Impact of College Students’ Sports on Mental Health (Project No.: 20JK0288).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Chen, C., You, K. (2023). A Method of Abnormal Psychological Recognition for Students in Mobile Physical Education Based on Data Mining. In: Fu, W., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-28867-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-28867-8_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28866-1
Online ISBN: 978-3-031-28867-8
eBook Packages: Computer ScienceComputer Science (R0)