Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between source and target point clouds. Existing methods obtain correspon-dences directly by computing point-wise feature similar-ity between point clouds. However, non-rigid objects pos-sess strong deformability and unusual shapes, making it a longstanding challenge to directly establish correspon-dences between point clouds with unconventional shapes. To address this challenge, we propose an unsupervised Template-Assisted point cloud shape correspondence Net-work, termed TANet, including a template generation mod-ule and a template assistance module. The proposed TANet enjoys several merits. Firstly, the template generation mod-ule establishes a set of learnable templates with explicit structures. Secondly, we introduce a template assistance module that extensively leverages the generated templates to establish more accurate shape correspondences from multiple perspectives. Extensive experiments on four hu-man and animal datasets demonstrate that TANet achieves favorable performance against state-of-the-art methods.