Evaluation Model of Classroom Teaching Quality of Chinese as a Foreign Language Based on Deep Learning

Abstract

In order to improve the classroom teaching quality of Chinese as a foreign language and attract more foreigners to participate in Chinese learning, a model for evaluating the quality of Chinese as a foreign language based on deep learning is proposed. Through the study of macro and micro indicators such as teaching methods, teaching tools, teaching content, curriculum relevance, teaching attitudes, etc., a classroom teaching quality evaluation index system is established to improve the coverage of quality evaluation. Based on the quantification of the processing qualitative indicators of deep learning, the deep learning data flow diagram is drawn according to the depth of different quality nodes in the teaching behavior, and the quality evaluation results is classified to complete the comprehensive evaluation of classroom teaching quality. In addition, by calculating the vector value of classroom teaching quality, planning experiment groups, and setting up comparative experiments, it is verified that the proposed model is more practical in the actual classroom teaching quality evaluation.