In view of the problem that general pre-trained models are not suitable for named entity recognition tasks in the medical domain

a neural network architecture that integrates knowledge graph in the medical domain was proposed. The elastic position and masking matrix were used to avoid semantic confusion and semantic interference in self-attention calculation of pre-trained model. The idea of multi-task learning in fine-tuning was adopted

and the optimization algorithm of recall learning was employed for pre-trained model to balance between general semantic expression and learning of the target task. Finally

a more efficient vector representation was obtained and label prediction was conducted. Experimental results showed that the proposed architecture achieved better results than the mainstream pre-trained models in the medical domain

and had relatively good results in the general domain. The architecture avoided retraining pre-trained models in particular domain and additional coding structures

which greatly reduced computational cost and model size. In addition

according to the ablation experiments

the medical domain was more dependent on the knowledge graph than the general domain

indicating the effectiveness of integrating the knowledge graph method in the medical domain. Parameter analysis proved that the optimization algorithm which used recall learning could effectively control the update of model parameters

so that the model retained more general semantic information and obtained more semantic vector representation. Besides

the experimental analysis showed that the proposed method had better performance in the category with a small number of entities.