Treating the shape contour as an unordered point set and extracting shape features from it for fast and effective shape recognition is a challenge task of shape analysis. To address this issue, a complex-network based shape description and recognition method is proposed in this paper. In this method, a self-organized dynamic network-evolution model is built for providing a hierarchical description framework. In each moment of the dynamic evolution of the complex network, local and global measurements are performed against the network shut that both weighted and un-weighted features are extracted from the network. At the shape matching stage, the local matching (based on Hausdorff distance) and global matching (based on L₁ distance) are conducted using the obtained local descriptor and global descriptor respectively. The dissimilar value between two shapes is determined by combining the two distance measures. Several standard test sets are used to evaluate the performance of the proposed method, and the experimental results show that the proposed method can provide robust and fast shape recognition in high accuracy.