In the field of natural language processing, Named entity recognition (NER) is a essential task. Mathematical formulas usually contain a large number of terminologies, units of measure and other proprietary knowledge, and the integration of this information into the knowledge graph can significantly enhance the semantic expression ability of the graph. By identifying the named entities in data formulas, the key concepts, entities and relationships between them in the knowledge graph can be extracted, establishing basis for the construction of the knowledge graph and making it easier to interpret and analyse in practical applications. Furthermore, the structured knowledge derived from this process can facilitate personalized learning path recommendations by mapping identified entities to educational resources and prerequisite relationships. Aiming at the problem of insufficient recognition ability of existing models for mathematical formula entities, a mathematical formula named entity recognition method combining enhanced dynamic allocation of labels is proposed. A mathematical formula entity recognition model consisted of BERT(Bidirectional Encoder Representation from Transformer), BiLSTM(Bidirectional Long Short-term Memory) and Transformer was constructed, namely BERT-formula. The feature representation of deep semantic information is enhanced by adding extra sequences to the original vector representation for splicing at the model input; and the entity label prediction problem is regarded as a one-to-many linear allocation problem, and an auction algorithm is introduced to acquire the optimal allocation result with the smallest cost. Experiments demonstrate that the accuracy of the model prediction on the mathematical formula set is 98.8%, and the F1 value is 98.8%, which is improved by 1.51 and 1.05 percentage points compared with BERT-BiLSTM-CRF. It is evident that the approach performs well on the objective of identifying mathematical formula entities.