[Objective] This paper proposes a supplementary question and answer (Q&A) recommendation method based on the transfer learning enhanced multi-label multi-document Q&A classification model, aiming to identify and recommend supplementary Q&A in community question answering.

[Methods] We proposed new features and classified Q&A supplementary relationships combining existing features. Then, we built a transfer learning enhanced multi-label multi-document Q&A classification model to identify and recommend supplementary Q&As.

[Results] Experiments are conducted on three meta-tasks using real datasets. Results demonstrate that the proposed method improves 48.3%, 15.8%, and 32.5% in the precision, recall, and f-measure separately.

[Limitations] The method has only been applied to health Q&A topics on the Zhihu platform. The performance on different platforms with different topics of Q&A remains to be verified.

[Conclusions] The proposed recommendation method can effectively recommend supplementary Q&As. It helps users in community question answering acquire knowledge more comprehensively and promote knowledge utilization in the community.