(1.重庆大学 管理科学与房地产学院,重庆 400045;2.重庆大学 体育学院,重庆 400044) 摘 要:为探究身体活动与积极心理品质之间的关系,对重庆5所高校大学生进行有关身体活动与积极心理品质的客观评定,运用层次聚类算法和深度神经网络模型,分析不同强度身体活动量对大学生心理积极品质的影响。结果表明:(1)大学生参与中高强度的身体活动越多,其积极心理品质水平越高;(2)高强度身体活动对人际、公正、认知的影响最有效;(3)中等强度身体活动对节制、情感、公正的影响最有效;(4)严格遵循身体活动推荐量的大学生超越品质更高。研究认为,大学阶段是改善及养成良好心理和积极品质至关重要的时期,提供体育活动干预可能是帮助大学生培养积极心理品质的重要途径。 关 键 词:身体活动;积极心理品质;高校学生;聚类分析;深度神经网络 中图分类号:G804.8 文献标志码:A 文章编号:1006-7116(2023)04-0118-06 |
HU Nan1,HUANG Dandan2,ZHENG Hongzhu1,HU Hong2
(1.School of Management Science and Real Estate,Chongqing University,Chongqing 400045,China; 2.School of Physical Education,Chongqing University,Chongqing 400044,China)
Abstract: To explore the relationship between physical activity and positive mental characters, this paper uses a questionnaire survey and a scale assessment on physical activity and positive mental characters were carried out to objectively assess physical activity and positive mental characters among college students from five universities in Chongqing. Hierarchy cluster analysis and deep neural network model is used to analyse the effects of the amount of physical activity with different intensities on positive mental characters among college students. The results show that: (1) The more medium and high intensity physical activity college students hold, the higher level of positive mental characters they will possess. (2) The high-intensity physical activity has the most effective effect on interpersonal relationship, justice, and cognition. (3) The moderate-intensity physical activity has the most effective effect on temperance, emotion and justice. (4) College students who strictly follow the recommended physical activity amount have a higher transcendence character. It can be concluded that the stage of college is a most important period that college students could improve and develop positive mental characters, and that providing physical activity intervention may be a vital way to help them cultivate such positive mental character. Keywords: physical activity;positive mental character;college students;cluster analysis;deep neural networks |
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