A Data-Driven Approach to Optimizing Teaching Effectiveness in the Field of Chinese Language Chinese Education

Taking the process or result data of Chinese language and Chinese education as the research object, this paper provides strategies for the way of teaching Chinese language and Chinese language in colleges and universities by deeply mining the rich teaching big data through data mining technology. Through the correlation analysis method, the connection between courses is clarified, so as to guide students in choosing courses and help teachers optimize their teaching plans. The study shows that in the word frequency analysis, the keywords of Chinese as a foreign language (1184), Chinese international education (1142) and Chinese language and literature (1095) are most in line with the demand of the job market. Teaching is at the center of the semantic network, so teaching ability is the most basic requirement in the job market. Accordingly, optimization strategies for teaching effect, such as deepening school-enterprise joint training, are proposed. In the correlation analysis of basic courses, the correlation coefficient between advanced Chinese translation and China’s international relations is 0.611, which indicates that more attention should be paid to the practical skills related courses in the curriculum, so as to improve the teaching effect.