Transformer　has　good　feature　extraction　ability　and　has　achieved　good　performance　on　various　NLP　tasks　such　as　sentence　classification,　machine　translation　and　reading　comprehension,　but　it　does　not　perform　well　in　named　entity　recognition　tasks.　According　to　recent　researches,　the　Long　Short-Term　Memory　(LSTM)　usually　performs　better　than　Transformer　in　NER　task.　LSTM　is　a　variant　of　Recurrent　Neural　Network　(RNN),　because　of　its　natural　chain　structure,　it　can　learn　the　front　and　back　dependencies　between　words　well,　which　is　very　suitable　for　processing　text　sequences.　In　this　paper,　the　BiLSTM　network　structure　is　embedded　into　the　Transformer　Encoder,　and　a　new　network　structure　BiLSTM-IN-TRANS　is　proposed,　which　combines　the　sequential　feature　extraction　capability　of　BiLSTM　and　the　powerful　global　feature　extraction　capability　of　Transformer　Encoder.　The　experiments　can　reflect　that　applying　the　model　based　on　BiLSTM-INTRANS　could　work　better　than　applying　one　of　LSTM　or　Transformer　alone　in　the　NER　task.　©　2022　SPIE.