Abstract
The COVID19 pandemic has had acute and long-term effects on college students’ mental health, but there was a lack of research following. This study aims to compared research hot spots of college students’ anxiety during and before the epidemic. By analyzing 2,350 pieces of literature from the Chinese National Knowledge Infrastructure (CNKI), researchers utilized LLR clustering algorithms, Price core author algorithm, and Donohue high and low frequency word algorithms to predict future development trends. The results showed that research on college students’ anxiety has changed in research hot spots before and after the epidemic, with increase rapidly following the COVID-19 outbreak. Firstly, a core author group was not formed, and most institutions had few collaborations due to geographical restrictions. Secondly the hotspots of research were on the common psychological problems of college students before the epidemic, and after the epidemic they were on the loneliness, information anxiety, sleep, and social support caused by the epidemic, which can be summarized into four aspects of emotional, cognitive, behavioral, and physiological problems. These findings highlight the need for further study of college students’ anxiety problems in the post-epidemic era.
The study was supported by the Jilin Provincial Administration of Traditional Chinese Medicine (No.: 2022223) and Changchun University of Chinese Medicine (No.: 2021KC20)
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Wen, H., Xu, M., Gong, Y., Zhang, H., Zhu, X. (2024). Comparisonof Chinese College Students’Anxiety During and BeforeCOVID19 Pandemic Based on Clustering Algorithm. In: **, H., Pan, Y., Lu, J. (eds) Artificial Intelligence and Machine Learning. IAIC 2023. Communications in Computer and Information Science, vol 2058. Springer, Singapore. https://doi.org/10.1007/978-981-97-1277-9_32
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