The existing three-dimensional shape local features are mostly lack of spatial structure information and only contain a single property. In order to solve the problems, a hierarchical feature extraction framework integrating topological connection information of three-dimensional shape was proposed to obtain the three-dimensional shape ring feature with shift invariance. Firstly, based on the low-level feature extraction of a three-dimensional shape, the local region of feature points was modeled by the way of the isometric geodesic ring, which could extract the middle-level feature containing rich spatial geometric structure information. Then, the middle-level feature was further abstracted by using sparse coding to obtain more discriminative high-level feature with abundant information. The obtained high-level feature was compared with the existing Scale Invariant Heat Kernel Signature (SI-HKS) in two tasks of three-dimensional shape correspondence and shape retrieval, and its accuracy was increased by 24.5 percentage points and 7.2 percentage points respectively. The experimental results show that the proposed feature has higher resolution and recognition than the existing feature descriptors.