As a huge knowledge network diagram， the knowledge graph contains entity concepts， relationships， and other information. Although the semantic representation based on deep learning has strong generalization， it is not sensitive to some proprietary knowledge， so many researchers try to combine knowledge graphs with neural network. At present， most of the methods of semantic representation of knowledge graphs are based on general domain knowledge graphs， and there is no research on the semantic representation of knowledge graphs in the academic field. In this paper， the full-text data of academic literature is taken as the research object， and the semantic representation method based on an academic knowledge graphs is studied. On the basis of constructing academic knowledge graph， the research method of the general field （K-BERT） is improved （KEBERT）， and entity knowledge is further used to enhance the semantic information of the text. By conducting comparative experiments on downstream tasks， the performance of KEBERT， K-BERT， and ERNIE is verified on academic retrieval datasets. The experiment uses the NDCG evaluation index commonly used in the retrieval task to evaluate the results. The experimental results show that the improved KEBERT is superior to other models in the retrieval task.