摘要：

Abstract:

【Purpose/significance】Automatic indexing of questions in Q&A community can provide effective help for information orga⁃

nization and information service of websites. At present, most researches on automatic indexing focus on extraction indexing, which is

not applicable to the automatic indexing of questions in Q&A community.【Method/process】Based on the CNGOLD Q&A community

as an example, this paper combines the methods of assignment indexing and extraction indexing, and proposes an automatic indexing

model of questions in Q&A community based on BERT and TF-IDF. This model uses the multi-label classification algorithm based on

BERT to assign the questions, divides the questions into short questions and long questions, and uses the TF-IDF algorithm to extract

the long questions and supplement the indexing tags of long questions.【Result/conclusion】 The experimental results show that the au⁃

tomatic indexing model proposed in this paper can effectively automatically index the questions in Q&A community, which is of great

significance to improve the effect of user information retrieval.【Innovation/limitation】Based on the internal and external characteris⁃

tics of questions, this paper constructs an automatic indexing model of questions in Q&A community based on BERT and TF-IDF, and

proposes a BERT based multi-label classification model.