Using long short-term memory (LSTM) networks to build a named entity recognition model is important for the task of named entity recognition. However, traditional memory networks lack a direct connection between input information and hidden states, leading to key feature information not being fully learned during training and causing information loss. This paper designs a bidirectional variant of the long short-term memory (BiLSTM) network called Mogrifier-BiGRU, which combines the BERT pre-trained model and the c…