Abstract
In this paper, TF-IDF algorithm is used to extract high-frequency keywords, LDA topic model is used to extract topic words, and TF-IDF vectorized text is used to calculate topic similarity. It is made a multi-dimensional quantitative analysis and qualitative research for 94 artificial intelligence education policies in China, and comprehensively studies the hotspots and topic evolution paths of artificial intelligence education policies. It was found that ‘ education ’, ‘ construction ’, ‘ development ’, etc.are the hotspots of policy in this field in the past ten years, and found the evolution law of themes in different stages. This clearly grasps the context and trend of the development of artificial intelligence education policy in China, and provides a good reference for the later policy formulation and adjustment.