Abstract: In the era of information technology,extracting structured information from massive natural language texts has become a research hotspot.The complex knowledge information in the power system needs to be solved by constructing a knowledge graph,and entity relation extraction is the upstream information extraction task,whose completeness directly affects the effectiveness of the knowledge graph.With the continuous development of deep learning,research on using deep learning techniques to solve entity relation extraction tasks has gradually been carried out and achieved good results.However,there are still problems such as incomplete application of text semantics.This paper attempts to propose an entity relation extraction method based on heterogeneous graph neural network and text semantic enhancement to address these issues.This method uses word nodes and relationship nodes to learn semantic features and obtains initial features of the two types of nodes through BRET and pre-training tasks respectively.It uses a multi-layer graph network structure for iteration and implements the interaction between the two types of nodes by using multi-head attention mechanism for information transmission in each layer.Through experimental comparison with other models on two public datasets,this model achieves the expected effect and generally outperforms other entity relationship extraction models in various scenarios.

Key words: Deep learning, Natural language processing, Knowledge graph, Entity relation extraction, Heterogeneous graph neural networks, Text semantic enhancement