The invention discloses a visual question answering problem solving method based on a complex network analysis method, which includes semantic concept network construction, nonrandom depth walk, imageand text feature fusion and classifier, Semantic Concept Networks (SCNs) are designed to mine co-occurrence patterns of concepts to enhance semantic representation, non-random depth walk realizes themapping of complex networks to low-dimensional features, On the basis of constructing image semantic concept network, depth walk algorithm is used to learn the potential relationship of nodes in semantic concept network, and the nodes in complex network are mapped to a low-dimensional feature vector, and polynomial logistic regression is used to fuse image and text features to solve the visual question answering problem. The invention excavates the concept symbiosis mode and the hierarchical structure of the cluster concept, effectively integrates the visual and semantic features of the image, as well as the natural language features, and provides a feasible way for solving the visual question answering problem.