Traditional question answering methodsoften have problems of inefficiency and insufficient use of data information. In order to solve the problems above, in the entity recognition part, adversarial transfer learning was used to integrate the boundary information of Chinese word segmentation to improve the accuracy of entity recognition. At the same time, an entity labeling method was proposed based on global pointers instead of CRF to improve model training efficiency. In the predicate matching part, the Siamese network was used to solve the problem of insufficient semantic expression of sentence vectors obtained by using BERT directly. On the data set NLPCC-2016KBQA, an average F1 value of 85.99% was obtained, indicating the feasibility of this method.