Human motion prediction (HMP) is a crucial task in fields like human-computer interaction and autonomous driving, requiring accurate future motion prediction based on historical action data. However, most existing methods primarily focus on improving prediction accuracy, with much less attention on the robustness of models under varying viewpoints-a critical challenge in real-world applications. In this paper, we present RE-QGCN, namely Rotation-Equivariant Quaternion Graph Convolutional Network, a novel human pose prediction model that achieves enhanced generalization and robustness due to rotation-equivariant properties. Building upon traditional Graph Convolutional Network (GCN), we propose Quaternion Graph Convolutional Network (QGCN), with S-QGCN and T-QGCN as specialized variants for spatial and temporal dimensions respectively, enabling efficient extraction of spatio-temporal quaternion features. Our approach offers a more streamlined and compact design compared to other rotation-equivariant models. Experiments demonstrate that RE-QGCN achieves complete rotation-equivariance while maintaining competitive accuracy, thereby establishing a strong benchmark for future research on rotation-equivariant HMP models.