Design of assessment algorithm and model for Chinese spoken language teaching based on natural language processing and knowledge graph

Abstract

The background of the research field on the design of assessment algorithms and models for Chinese spoken language teaching based on natural language processing and knowledge graph mainly involves two aspects: one is the growing global demand for learning Chinese, and the other is the potential application of advanced computing technology in language learning. The significance of this research lies in providing a more scientific and systematic assessment method for Chinese teaching through this system, and, on a macro level, paving the way for the future development of language learning technologies. Through this study, not only can teachers better guide students learning, but students can also receive more effective learning feedback and guidance. Experimental data shows that the accuracy rate of the Chinese spoken language teaching assessment algorithm system based on natural language processing and knowledge graph is 98.05%, and the satisfaction rate for personalized teaching evaluation reaches 98.12%. In summary, this research provides new methods and approaches for the personalization and technologization of Chinese spoken language teaching, thus having a profound impact on the field of language education.