NOTAM is an important way to notify changes in aeronautical intelligence information, and there are problems of non-correspondence between Q-code and E-item free text as well as non-standardization of expression in NOTAM. Aiming at the above problems, the bi-directional encoder representation technique- deep pyramidal convolutional neural network (BERT-DPCNN) NOTAM element similarity matching identification method was proposed. To address the above problems, based on the flight information Centre's 105 797 sample NOTAM from September 2020 to April 2023, the improved BERT-DPCNN navigational notice element similarity matching identification method was proposed to construct a text dataset with two types of data, E-code and Q-item, and to annotate the correctness of the text as well as the content of the wrong text correction. the E-item text was preprocessed by regularizing the E-item text using the bi-directional encoder representation technique (BERT) based on transformer, the global text features are extracted, and at the same time, the text encoding was carried out on the Q-code, and the pre-training text vector set was generated. The trained vector set was input into the deep pyramidal convolutional neural network (DPCNN) model as a word embedding layer, and 60% of the training set, 20% of the testing set and 20% of the validation set are randomly selected for model training, and then the trained model is used to discriminate the text similarity, and the results of the model evaluation metrics show that the average precision of all types of navigational notices is 88.77%, the recall rate is 88.74%, the F₁-score is 89.50%, the recognition effect is better than Text CNN, BERT, ERNIE, BERT-CNN models.