Research on Personalized Course Recommendation Model for Preschool Education Assisted by Deep Learning Algorithm

This paper proposes a personalized course recommendation model for preschool education based on deep learning algorithm. The model combines DNN and CNN technology, and realizes accurate recommendation of personalized courses by deeply mining multidimensional data such as children's age, interests, and cognitive level. Then this paper constructs a hybrid recommendation algorithm based on collaborative filtering and content recommendation to further improve the accuracy and user experience of the recommendation system. By comparing and optimizing different deep learning models, the optimal model is finally selected for simulation testing. The simulation results show that the proposed recommendation model has significant improvements in recommendation accuracy, user satisfaction, and learning effect. Specifically, compared with the traditional rule-based recommendation method, the deep learning algorithm improves the accuracy and pertinence of course recommendations, and its recommendation accuracy reaches more than 90%. Data analysis shows that with the improvement of the accuracy of the recommendation algorithm, children's participation in course learning and learning outcomes have also been significantly improved.