Medical text has high entity density and verbose sentence structure, which makes the simple neural network methods unable to capture its semantic features. Therefore, a hybrid neural network method based on pre-trained model is proposed. Firstly, a pre-trained model is used to obtain the dynamic word vector and the entity tagging features are extracted. Secondly, the contextual features of the medical text are obtained through a bidirectional long and short-term memory network. Simultaneously, the local features of the text are obtained using the convolutional neural network. Then the global semantic features of the text are obtained by weighting the sequence features through the attention mechanism. Finally, the entity tagging features are fused with the global semantic features and the extraction results are obtained through the classifier. The experimental results of entity relation extraction on the medical domain dataset show that the performance of the proposed hybrid neural network model is improved compared with the mainstream models, which indicates that this multi-feature fusion method can improve the effect of entity relation extraction.