The invention discloses a network rumor detection method based on a multi-modal relationship. The network rumor detection method comprises the steps of obtaining a to-be-detected image and a related text published on a network platform; extracting visual feature vectors containing different types of objects in the image through a pre-training factor R-CNN model; after the text is preprocessed, performing semantic vector extraction through a GRU; capturing importance degrees of the visual feature vector and the semantic vector through an attention mechanism, and realizing cross-modal association between the image and the text so as to update the visual feature vector and the semantic vector; moreover, for the visual feature vector and the semantic vector, the relationship of internal dynamic information is modeled through an attention mechanism, so that the visual feature vector and the semantic vector are updated; and connecting the visual feature vector and the semantic vector obtained by updating the two parts together, and obtaining the probability that the information to be detected is the rumor and the real category through a binary classifier. The method can automatically judge whether the to-be-detected information belongs to the network rumors, and has relatively high detection accuracy.
Network rumor detection method based on multi-modal relationship