System Evaluation and Management of College Students’ Physical Exercise Behavior Stages Integrating Bayesian Association Rules Data Mining Algorithm

Abstract

The form of association rules is simple, and it is efficient and convenient to apply. However, because association rules cannot express the connection between different rules, in some more complex application fields, when it is necessary to comprehensively consider the impact of multiple factors on the results, the application of rules is more difficult. In the process of reasoning about the node state, the influence of various factors (parent nodes) can be comprehensively considered. This paper proposes a Bayesian network-based association rule representation method. After mining the association rules from the data, through structural learning and conditional probability table learning, the original rules are finally used as Bayesian nets. This effectively expands the application of association rules. The experimental results show that after using MapReduce parallelization, the improved algorithm can not only process larger-scale data sets, but also save a lot of running time. The correlation between physical exercise behavior evaluation management, dimensional exercise motivation, exercise method, and college students’ school adaptability reached a significance of 0.001, and the correlation between dimension exercise time and college students’ school adaptability reached a significance of 0.05. The correlation between physical exercise behavior evaluation management and school adaptability in terms of interpersonal relationship adaptation, learning adaptation, campus adaptation, career adaptation, emotional adaptation, self-adjustment, and satisfaction reached significant levels.