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The essence of connection in vehicle network is the social relationship between people, and thus Vehicular Social Networks (VSNs), characterized by social aspects and features, can be formed. The information collected by VSNs can be used for context prediction of autonomous vehicles. Multivariate relations are common in square connected relations caused by geographic characteristics in VSNs. They can effectively reflect the high-order structural features of the network dataset. It is necessary to exploit the multivariate relations of VSNs to improve the performance of context prediction. However, The representation of entity-relationes in the network often adopts a binary form, and the existing graph learning methods rely on the neighborhood information of nodes to achieve the aggregation or diffusion of information. Using this to represent multivariate relations will result in partial omissions or even complete loss …