Similarity Calculation of Concepts Based on Feature Distillation

Aiming at the problem that the accuracy of similarity calculation results is not high due to the lack of semantic information in the standard terminology database, this paper proposes a conceptual similarity method based on feature distillation. The method firstly utilizes the FastText model to obtain the word vectors of the text, and then recalculates and weights to get new word vectors using the resources of the standard terminology database, and finally adds the BiLSTM model to further extract the contextual semantic information. The experimental results show that the method effectively integrates domain knowledge, enhances the recognition ability of text semantics, and significantly improves the accuracy of the similarity calculation results between texts in the standard terminology database.