In order to reduce the large amount of work that traditional named entity recognition needs to manually formulate features, we obtain distributed vector representations of the military domain corpus through unsupervised training, and utilize the bidirectional LSTM (BLSTM) recursive neural network model to solve the identification problem of named entities in the military field. The BLSTM recursive neural network model is extended and improved by adding wordbinding input vectors and attention mechanism to enhance the recognition of named entities in the military field. Experimental results show that the proposed method can identify named entities in the military field, and the Fvalue in the test set corpus reaches 87.38%.