In the field of information extraction, it is a basic and important task to extract entity relations from unstructured texts, and challenges such as entity overlap and model error accumulation often appear. This study is relation-oriented, and it proposes an improved joint extraction method for entity relations. The method divides the entity relation extraction task into two subtasks: relation extraction and entity extraction. For the relation extraction subtask, a self-attention mechanism is adopted to evaluate the degree of association between words, so as to simulate entity information and represent the whole sentence information by the average pooling. For the entity extraction subtask, according to relation information, the conditional random field is used to identify the entity pairs under the relation. This method can not only solve the problem of entity overlap by using the idea that relation and entity pairs coexist but also perform training by using the known relation in the dataset to make the entity extraction module independent from the results of the relation extraction module during the training, so as to avoid error accumulation. Finally, the effectiveness of the model is verified on the public datasets of WebNLG and NYT.