Learning has been a significant emerging field for several decades since it is a great determinant of the world’s civilization and evolution, having a significant impact on both individuals and communities. In general, improving the existing learning activities has a great influence on the global literacy rates. The assessment technique is one of the most important activities in education since it is the major method for evaluating students during their studies. In the new era of higher education, it is clearly stipulated that the administration of higher education should develop an intelligent diversified teaching evaluation model which can assist the performance of students’ physical education activities and grades and pay attention to the development of students’ personalities and potential. Keeping the importance of an intelligent model for physical education, this paper uses factor analysis and an improved random forest algorithm to reduce the dimensions of students’ multidisciplinary achievements in physical education into a few typical factors which help to improve the performance of the students. According to the scores of students at each factor level, the proposed system can more comprehensively evaluate the students’ achievements. In the empirical teaching research of students’ grade evaluation, the improved iterative random forest algorithm is used for the first time. The automatic evaluation of students’ grades is achieved based on the students’ grades in various disciplines and the number of factors indicating the students’ performance. In a series of experiments the performance of the proposed improved random forest algorithm was compared with the other machine learning models. The experimental results show that the performance of the proposed model was better than the other machine learning models by attaining the accuracy of 88.55%, precision of 88.21%, recall of 95.86%, and f1-score of 0.9187. The implementation of the proposed system is anticipated to be very helpful for the physical education system.

中文翻译：

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随机森林算法在体育教学中的应用

几十年来，学习一直是一个重要的新兴领域，因为它是世界文明和进化的重要决定因素，对个人和社区都有重大影响。总的来说，改进现有的学习活动对全球识字率有很大影响。评估技术是教育中最重要的活动之一，因为它是在学习期间评估学生的主要方法。新时代高等教育明确规定，高等教育行政管理部门要建立智能的多元化教学评价模式，辅助学生体育活动和成绩的表现，注重学生个性和素质的发展。潜在的。保持智能模型对体育教学的重要性，本文通过因子分析和改进的随机森林算法，将学生在体育教学中的多学科成绩维度缩减为几个有助于提高学生成绩的典型因素。根据学生在各个因素水平的分数，所提出的系统可以更全面地评估学生的成绩。在学生成绩评价的实证教学研究中，首次使用了改进的迭代随机森林算法。学生成绩的自动评价是根据学生在各个学科的成绩和表明学生表现的因素的数量来实现的。在一系列实验中，将所提出的改进随机森林算法的性能与其他机器学习模型进行了比较。实验结果表明，所提出模型的性能优于其他机器学习模型，准确率为 88.55%，准确率为 88.21%，召回率为 95.86%，以及f 1 分 0.9187。预计拟议系统的实施将对体育系统非常有帮助。