Development of an Intelligent Retrieval and Recommendation System for Chinese Language Educational Resources Based on DNN Technology

With the increasing global demand for Chinese language learning, the volume of online educational resources has grown exponentially. However, traditional keyword-based retrieval methods often fail to capture the semantic relationships in user queries, leading to low retrieval accuracy and lack of personalized recommendations. To address these challenges, this study presents the development of an intelligent retrieval and recommendation system for Chinese language educational resources based on deep neural networks (DNN). The proposed system leverages state-of-the-art natural language processing (NLP) techniques to enhance semantic understanding and optimize resource matching. The retrieval mechanism integrates DNN based semantic search, while the recommendation engine combines content based filtering, collaborative filtering, and reinforcement learning to provide personalized suggestions. A comprehensive evaluation was conducted using a dataset of Chinese language learning materials, with user experiments assessing retrieval effectiveness, recommendation accuracy, and system efficiency. Results demonstrate that the proposed DNN based system significantly outperforms conventional retrieval and recommendation methods in terms of precision, recall, and user satisfaction. This research contributes to advancing AI driven educational technologies, providing an adaptive and efficient approach for enhancing Chinese language learning experiences.