A network community detection algorithm SYN, is proposed based on Kuramoto model which is a dynamic model of synchronization. Firstly, the vertices in a network are sorted according to the link densities between vertices. As a result, each vertex is projected to a one-dimensional value and the network is transformed to a vector data. During the clustering process, the data are synchronized within a local region and the data points synchronized together will be considered as a community. By enlarging the radius of synchronization, our method can detect the multi-resolution community structure of a network. Through the modularity function, our method can automatically select the optimal clustering result. Our method does not depend on any data distribution assumptions and it can detect communities of arbitrary number, size and shape in networks. The experimental results on a large number of real-world and synthetic networks show that our method achieves high accuracy.