Entity relationship extraction is one of the key tasks of information extraction, which involves a multi-task cascade including entity extraction and relationship extraction. Traditional methods of entity relationship extraction follow a mode of Pipeline which separates entity extraction from relationship extraction, ignoring the internal connection between the two. As a result, the effect of relationship extraction depends heavily on entity extraction, and it is prone to error accumulation. To avoid this problem, we propose an end-to-end joint entity and relationship extraction model, which relies on the self-attention mechanism to learn word features, constructs dependency constraints based on dependency information contained in syntactic dependency graphs, and then integrates constraint information into a graph attention network for entity and relationship extraction. Experiments on the public data set NYT demonstrate the advance and significance of our model which has a high recall rate and better extraction performance than previous methods.