目的 传统的基于欧氏距离的复杂网络表示方法容易受形状的非刚性变形影响。鉴于此，提出一种基于复杂网络模型与相对一致性距离相结合的形状特征提取方法。方法 首先，提取形状的边界轮廓点作为网络的节点，利用节点间的相对一致性距离作为边的权值构建初始的复杂网络模型；然后，利用阈值演化方法对初始网络模型进行动态演化，得到一系列子网络；最后，提取不同演化阶段下子网络的拓扑特征，实现对形状特征的提取。结果 分类和检索实验结果表明，相比于传统的复杂网络描述方法，本文方法对形状图像具有更强的描述和识别能力。结论 相比于传统的距离度量，相对一致性距离对形状的非刚性变形具有更强的稳定性。

Objective Traditional Euclidean distance based complex network is usually sensitive to the non-rigid transformation of the shape image. To overcome this problem, in this paper, a novel shape feature extraction Method based on complex network model and relative coherent distance is proposed. Method First, an initial complex network is constructed with nodes corresponding to the boundary points and edges allocated with relative coherent distance as weights existing between each node pairs. Then, this initial network is threshold evolved to generate a series of sub-networks. At last, some topological features are extracted from these sub-networks to generate the feature descriptors for the shape image. Result Promising experimental Results on classification and retrieval show that the proposed Method has strong capability in discriminating and recognizing variety of object shapes. Conclusion Comparing with the traditional distances, the relative coherent distance is more robust to the shape non-rigid and elastic transformations.