This study focuses on the potential impact of new media language on the semantic evolution of Chinese, constructs the Chinese Lexical Semantic Core Knowledge Base (CLSKB_Core), which integrates the resources of multi-source dictionaries, and puts forward a semantic similarity computation model based on conceptual graphs. The semantic evolution of Chinese in the new media context is explored through the analysis of ephemeral frequency and the validation of the co-conceptual map retrieval algorithm. The experiments show that the algorithm recognizes sentence types with simple structure such as definite middle, preposition, number, gerund, etc. with better efficiency, and the average correctness rate reaches 0.899. In eight types of problems, the average Fβ value reaches 67.025%. For concept maps with imbalanced attribute descriptions, the algorithm in this paper achieves an average compatibility of 0.886, which is 0.6% higher than the original algorithm. The semantic migration trajectory of the typical case “Tuhao” reveals the penetration mechanism of Internet buzzwords into the semantic field of traditional vocabulary, and reflects the feasibility of adopting natural language processing algorithms to study the semantic change of Chinese.
