Abstract: In online social networks, massive amounts of information are transmitted and diffused through users’ interaction and reposting behavior. As the carrier of information diffusion, social media can not only make people share information flow and get current affairs news quickly, but also facilitate the exchange of ideas and information between people. At the same time, it may become an important channel for the spread of false information. Most of the existing researches on false information detection are based on the recognition models of machine learning and deep learning of Weibo content, while ignoring the structural differences between true and false information networks. Therefore, based on the motif theory of complex networks, this paper puts forward the concepts of breadth and depth motif degree to quantify the structural importance of the network. The research shows that the importance calculation method based on motif degree is an innovation and expansion of traditional network structure importance index, which can measure the specificity of communication network structure more comprehensively. This paper analyzes and reveals the structure characteristics and propagation mechanism of false information in microblog network by constructing the two-dimensional motif measurement index, that is, the false information is diffused under the joint action of breadth and depth propagation, and the breadth motif mainly affects the network spread scale, while the depth motif degree affects the complexity of the network structure. Even in the early stage of information diffusion, the false news detection method based on motif features has a high prediction accuracy. The network feature analysis based on motif degree can be applied to detect false information from the source in the early stage of social media information diffusion, which provides a novel and feasible way for false information detection.