The invention relates to a question-answering method based on knowledge graph completion, and belongs to the field of natural language processing, and the method comprises the following steps: S1, dividing input Q into words or phrases; s2, representing the words as vectors by using a word vector model BERT to obtain a matrix as model input; s3, identifying entities in the Q by using an entity identification technology to obtain a candidate entity set; s4, querying the category of the eKGs, and replacing the entities in the Q with c; s5, constructing a declarative query cyber, and obtaining acandidate triple set so as to obtain a candidate relationship set; s6, linking based on the relationship between Qc and rij; s7, in the KGs, if the relationship between the eKGs and the rij is absent;s8, learning new vector representation of entities eKGs and entities in neighborhoods of the eKGs; s9, estimating the importance of entities in the neighborhood of the central entity; s10, executingrelation prediction on the basis of existing related triples; and S11, obtaining an answer A based on knowledge graph reasoning of entities and relationships.