The invention discloses a knowledge reasoning method based on a multi-
modal knowledge graph, and aims to enable knowledge reasoning reliability and accuracy to be higher and enable the knowledge reasoning method to have stronger modeling and reasoning capabilities. The method is realized through the following technical scheme: different information is fused based on multi-hop reasoning of a large-scale
knowledge base; attribute completion is performed on the attribute missing graph through attribute
graph embedding, structured information is extracted from unstructured and semi-structured documents or sentences, and a dynamic heterogeneous
graph embedding model is constructed for multi-type characteristics of the multi-
modal knowledge graph through heterogeneous
graph embedding;
feature learning of semi-structured knowledge, structured knowledge and different types of non-structured knowledge is achieved, and multi-
modal knowledge graph features are obtained and serve as input for knowledge reasoning based on a graph neural network GNN; an
inference path is generated, and a plurality of types of
inference paths are constructed; and classification, edge prediction and frequent subgraphs of node types are calculated on the graph, a knowledge reasoning task is generated, and multi-step complex knowledge reasoning is completed.