English teaching is an important part of cultivating high-quality skilled talents and one of the main positions for spreading Chinese culture. This paper proposes a teaching strategy to integrate Chinese traditional culture into the English curriculum, and at the same time, combined with artificial intelligence technology, it constructs a Dynamic Key-Value Memory Network (DKVMN-F) based on forgetting behaviors in sequences and a Neural Network-based Recommendation Model for Similar English Topics (HANN), which realizes the in-depth tracking of the students’ English knowledge level and personalized and intelligent recommendation of English topics. On this basis, a game task-driven English teaching model based on learner profiles is innovatively designed. Compared with current knowledge tracking models, DKVMN-F performs more accurately in tracking students’ knowledge mastery status and has better prediction performance by introducing features such as repetition interval and number of past answers for modeling. On both the ML-1M and Gowalla datasets, the HANN model achieves a performance improvement of 0.5%-7.5% relative to the other models. On the ASSISTments2009 dataset, the HANN model achieves the optimal value in all evaluation metrics by adding an exercise similarity attention mechanism. The research in this paper provides a teaching model reference for the use of artificial intelligence technology to realize the organic integration of traditional culture and English teaching and to improve the teaching effectiveness.