NOTAM (notice to airman) is important information in the field of civil aviation intelligence. In view of the problems such as more professional terms, inconsistent format and complex semantics of Chinese NOTAMs (notices to airman), an entity recognition model based on BERT-Bi-LSTM-CRF was proposed to extract the event element entities from the E items of NOTAMs. The word vector was pre-trained by the bidirectional encoder representations from transforms (BERT) model to capture rich semantic features and input into the bidirectional long and short-term memory network (Bi-LSTM) model to extract contextual features, and finally the conditional random field (CRF) model was used to output the best Entity prediction labels. The original corpus related to airport-like navigation announcements was collected and organized, and after data pre-processing and text annotation, the training set, validation set and evaluation set data were formed which can be used for entity recognition experiments. Based on this data and different entity recognition models for comparison experiments, the accuracy of the BERT-Bi-LSTM-CRF model is 89.68%, the recall rate is 81.77%, and the F₁ value is 85.54%, where the F₁ value is effectively improved compared with the existing models, and the results validate the effectiveness of the model for elemental entity recognition in airport-like navigational announcements.