Research on Foreign Language Teaching Effect Prediction Model Based on Deep Learning

This paper proposes a predictive model for foreign language teaching effectiveness based on deep learning. Firstly, the paper introduces the application of label transfer technology in cross-language environment, and realizes the efficient conversion of labels between different languages through deep learning algorithm, which lays a foundation for the subsequent entity extraction and translation work. Through the combination of shared presentation layer and task-specific layer, the optimization of multiple tasks such as label migration, cross-language entity extraction and self-learning dictionary translation is realized. We use deep neural networks to automatically identify and extract key entity information from multiple languages. At the same time, the self-learning dictionary translation technology is introduced to improve the accuracy and efficiency of translation through continuous learning and optimization. A prediction model for foreign language teaching effect is constructed and its validity and accuracy are verified by many experimental data. The experimental results show that the model can effectively predict the effect of foreign language teaching and provide an important reference for teachers and students.