The present application relates to the field of natural language processing in artificial intelligence. Disclosed is a knowledge graph question-answer method based on deep learning technology. The method comprises: receiving a question statement of a user; performing entity recognition on the question statement by using an LSTM+CRF-based sequence labeling model to obtain entity information from the question statement; using an attribute recognition model to perform attribute recognition on the question statement to obtain attribute information from the question statement; performing attribute expansion and attribute standardization on the attribute information to obtain a corresponding standard attribute in a knowledge graph; and generating a structured query for the knowledge graph according to the entity information and the standard attribute, querying the knowledge graph for an answer, and returning the found answer to the user. Models and knowledge graph information can be stored in a blockchain. According to the present application, semantic information of an entity layer, a phrase layer and a question layer is sufficiently integrated into models by means of multiple Bi-LSTM layers and an Attention operation, thereby improving the effect of the models and the question-answer accuracy.
KNOWLEDGE GRAPH QUESTION-ANSWER METHOD AND APPARATUS BASED ON DEEP LEARNING TECHNOLOGY, AND DEVICE