Abstract: Graph-based fake review detection primarily faces the notoriously challenge of effectively aggregating neighbor information from different relations particularly in an attributed graph with only a limited number of labeled positive samples. The goal is to enhance the sensitivity of graph representation learning to anomalous nodes and improve its generalization capability. To address this challenge, this paper proposes a meta-learning-based multi-information fusion graph difference network (Meta-MGDN). By innovatively constructing a multi-view partitioning and multi-information fusion module, the structural and attribute information of users, items, and rating relationships are fully explored to enable the network to capture multi-faceted information and uncover and analyze relationships between review nodes. This paper designs a multi-view neighborhood difference aggregation module that integrates neighborhood information with self-neighborhood difference information, enabling the network to simultaneously capture both the correlation and the differences between nodes, thereby significantly enhancing its sensitivity to potentially anomalous review nodes. Finally, a meta-learning framework is introduced to simulate the few-shot problem using multiple auxiliary networks, which helps the network practically maintain a high generalization ability in the critical issue of few-shot fake review detection scenarios. Experiments on real-world datasets show that Meta-MGDN significantly outperforms the state-of-the-art baselines in the field of graph-based fake review detection.