In this paper, the cited papers and cited patents of world-class agriculture-related universities are taken as the research objects, and the preprocessing process of the research hotspot text of agriculture-related universities is accomplished by combining the participle processing method and the TF-IDF method. To make up for the shortcomings of the semantic level of the LDA model, BERTopic is utilized to obtain the document-topic distribution probability and the topic-topic word distribution probability, and the topic hotness of different topics is calculated. Then the Glove method is used to extract the word vector features of the topic words, normalize them, calculate the word vectors by taking the distribution probability values as the weighted weights, solve the topic vectors, and then calculate the similarity of the hot topics in different time windows by the cosine similarity to explore the hot topics and the evolution of the world’s first-class agriculture-related colleges and universities research.