Abstract: Relation extraction is an important part of information extraction technology, which aims to extract the relationship between entities from unstructured text. At present, entity relationship extraction based on deep learning has achieved certain results, but its feature extraction is not comprehensive enough, and there is still a large space for improvement in various experimental indicators. Entity relationship extraction is different from other tasks such as natural language classification and entity recognition. It mainly depends on the sentence and the information of two target entities. According to the characteristics of entity relationship extraction, this paper proposes the SEF-BERT model (Fusion Sentence-Entity Features and Bert Model). This model is based on the pre-trained BERT model. After the model is pre-trained by the BERT model, sentence features and entity features are further extracted. Then, the sentence feature and the entity feature are fused, so that the fusion feature vector can have the features of the sentence and two entities at the same time, which enhances the model′s ability to process feature vectors. Finally, the model was trained and tested using the data set of the general field and the data set of the medical field. The experimental results show that, compared with other existing models, the SEF-BERT model has better performance on both data sets.