In Named Entity Recognition （NER） of elementary mathematics， aiming at the problems that the word embedding of the traditional NER method cannot represent the polysemy of a word and some local features are ignored in the feature extraction process of the method， a Bidirectional Encoder Representation from Transformers （BERT） based NER method for elementary mathematical text named BERT-BiLSTM-IDCNN-CRF （BERT-Bidirectional Long Short-Term Memory-Iterated Dilated Convolutional Neural Network-Conditional Random Field） was proposed. Firstly， BERT was used for pre-training. Then， the word vectors obtained by training were input into BiLSTM and IDCNN to extract features， after that， the output features of the two neural networks were merged. Finally， the output was obtained through the correction of CRF. Experimental results show that the F1 score of BERT-BiLSTM-IDCNN-CRF is 93.91% on the dataset of test questions of elementary mathematics， which is 4.29 percentage points higher than that of BiLSTM-CRF benchmark model， and 1.23 percentage points higher than that of BERT-BiLSTM-CRF model. And the F1 scores of the proposed method to line， angle， plane， sequence and other entities are all higher than 91%， which verifies the effectiveness of the proposed method on elementary mathematical entity recognition. In addition， after adding attention mechanism to the proposed model， the recall of the model decreases by 0.67 percentage points， but the accuracy of the model increases by 0.75 percentage points， which means the introduction of attention mechanism has little effect on the recognition effect of the proposed method.