Optimization of Teaching Models System for Chinese Language Acquisition Based on Artificial Intelligence

The rapid development of artificial intelligence (AI) technology has brought new avenues for the innovation of Chinese language acquisition teaching models. This article focuses on constructing an intelligent teaching framework for Chinese language acquisition that integrates principles of cognitive science and AI algorithms and optimizes core learning recommendation algorithms. This framework is based on dynamic modeling of learners' language abilities, combined with knowledge tracking and adaptive learning mechanisms, and introduces a deep learning model based on attention mechanism to achieve fine-grained diagnosis and individualized path planning of learners' vocabulary, grammar, and pragmatic abilities. To verify the effectiveness of the model, a controlled experiment was conducted on a corpus of real Chinese language learners. The results show that compared with traditional teaching modes and benchmark recommendation algorithms, the proposed AI-driven teaching framework has significant advantages in improving learning efficiency, long-term memory retention, and stimulating learning motivation. This study provides a scalable and verifiable intelligent teaching paradigm for Chinese language acquisition and lays a theoretical and practical foundation for the deep application of educational AI in language teaching.