Interactive teaching is very popular in the field of education, especially in college preschool education teaching. In this study, the experimental performance of the evaluation system based on scientific emotion construction is analyzed by the machine learning algorithm. The experimental results show that the classification model realized by the machine learning algorithm is feasible and effective for the construction of an interactive design teaching evaluation system of preschool education in colleges and universities. The main research content of this experiment is for the teaching evaluation text, this paper intends to use NB, KNN, LR, and SVM algorithms as the meta-learning algorithm in the framework, the classification effect of the meta-classifier is ranked as SVM > LR > KNN > NB. From the above data comparison, the SVM meta-classifier in the algorithm in this paper has achieved the best classification effect. The SVM meta-classifier based on the algorithm in this paper performs well, and the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the. And the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the construction of interactive teaching evaluation systems under emotional education.

中文翻译：

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基于机器学习算法的高校学前教育互动教学评价系统

互动教学在教育领域非常流行，尤其是在大学学前教育教学中。本研究通过机器学习算法对基于科学情感构建的评价系统的实验性能进行了分析。实验结果表明，机器学习算法实现的分类模型对于构建高校学前教育交互设计教学评价体系是可行有效的。本实验主要研究内容为教学评价文本，本文拟采用NB、KNN、LR、SVM算法作为框架中的元学习算法，元分类器的分类效果排名为SVM > LR > KNN > NB。从以上数据对比来看，本文算法中的SVM元分类器取得了最好的分类效果。基于本文算法的SVM元分类器表现良好，分类准确率达到92.1%。由此可见，这个模型是非常适合研究的。分类准确率达到92.1%。这说明该模型非常适合研究情感教育下交互式教学评价系统的构建。