Abstract: In order to improve the efficiency of concept extraction in the field, a core concept extraction method based on fuzzy Bayesian decision-making is proposed. Firstly, after randomly extracting a large amount of text and sorting the text vocabulary, candidate concepts are obtained. Secondly, the characteristic values of the candidate concepts are calculated by the traditional TF-IDF algorithm, and normalized by the conceptual membership. Finally, the probability that the candidate concepts are the core concepts is calculated by Bayesian decision-making. The extraction experiment of the core concept of financial text shows that the average accuracy of core concept extraction is much higher than that of the traditional TextRank, LDA, word2vec, RNN and LSTM. Comprehensive experimental results show that the core concept extraction method based on fuzzy Bayesian decision-making performs better in core concept extraction. 

Key words: concept extraction, conceptual membership, Bayesian decision-making