The relationship extraction method based on remote supervision can cut the cost of labor-based annotated datasets and has been widely used in the construction of the domain knowledge graph. However, the existing remote supervised relationship extraction methods are not domain-specific and also neglect the utilization of domain entity feature information. To solve the above problems, this study proposes a relationship extraction model PCNN-EFMA that integrates entity features and multiple types of attention mechanisms. The model adopts remote supervision and multi-instance technology, no longer limited by labor-based annotation. At the same time, to reduce the impact of noise in remote supervision, the model uses two types of attention: sentence attention and inter-packet attention. In addition, it integrates entity feature information in the word embedding layer and sentence attention, enhancing the model’s feature selection ability. Experiments show that the PR curve of this model is better on the domain dataset, and its average accuracy on P@N is better than that of the PCNN-ATT model.