The Design and Development of an Ai-driven Chinese English Teaching Model

Abstract Global English has developed into a language spoken across the globe due to education. Chinese English seems to be quite important in this particular situation, since China is the nation with the greatest population that speaks and understands English. To gather qualitative data, six students were chosen at random to participate in individual discussions. To examine drawback of the existing Chinese private education system and provide qualified advice for improving the efficacy and rationality of English private education in China.To overcome this problem, we proposed a Scarlet Fox Boosted Deep Neural Network (SF-BDNN) method. The purpose of SF-BDNN method is to improve the Chinese English teaching model. Initially, we gather a Chinese English teaching dataset and secondly, the collected data is preprocessed using Min max normalization to clean the data. Thirdly, Term frequency Inverse Document Frequency (TF-IDF) method is employed to extract the unwanted data. Recursive Feature Elimination (RFF) is used to choose the appropriate data. The simulation findings of the result uses a Python tool. As a result, our proposed method achieves a superior result with listening skills (97%), Fluency English speaking (94%), Writing Proficiency (95.2), when compared to other existing methods.