Extracting entities and relations is crucial for building large-scale knowledge graph and different knowledge extraction tasks. Based on the pre-trained language model, an entity-oriented joint extraction method combining subject attention was proposed. In this method, the key information of the subject was extracted by using Convolutional Neural Network (CNN) and the dependency relationship between the subject and the object was captured by the attention mechanism. Followed by the above, a Joint extraction model based on Subject Attention (JSA) was built. In experiments on public dataset New York Times corpus (NYT) and the dataset of artificial intelligence built by distant supervision, the F1 score of the proposed model was improved by 1.8 and 8.9 percentage points respectively compared with Cascade binary tagging framework for Relational triple extraction (CasRel).