Abstract: 【Purpose/significance】Through the automatic extraction of concept hierarchies,a fine-grained domain semantic hierarchi‐cal system can be quickly and automatically obtained on a large data set,which provides a reference for the subsequent refined con‐struction of domain ontology.【Method/process】Firstly,based on the term set composed of compound terms and keywords,filtered by word frequency,text frequency and semantic similarity,the concepts of academic paper evaluation are obtained. Secondly,considering the co-occurrence relationship expressed by the paper-concept matrix and the concept co-occurrence matrix and the contextual se‐mantic information expressed by the word2vec word vector,the concept similarity matrix is obtained through similarity integration. Fi‐nally,based on concept similarity matrix,taking the concept with the largest correlation as the clustering center,the concept hierarchies
of academic paper evaluation are obtained through spectral clustering.【Result/conclusion】Experiment proves that the accuracy rate of the model proposed in this study is good,and the constructed academic papers evaluation domain hierarchical structure is reason‐able【. Innovation/limitation】This paper proposes an automatic extraction model of concept hierarchies based on word co-occurrence and word vector, which can automatically extract the hierarchies. However, the method for determining the class label is relatively simple and can be further explored.