Inspired from the idea of data fields, a community discovery algorithm based on topological potential is proposed. The basic idea is that a topological potential function is introduced to analytically model the virtual interaction among all nodes in a network and, by regarding each community as a local high potential area, the community structure in the network can be uncovered by detecting all local high potential areas margined by low potential nodes. The experiments on some real-world networks show that the algorithm requires no input parameters and can discover the intrinsic or even overlapping community structure in networks. The time complexity of the algorithm is O(m+n^3/γ)～O(n²), where n is the number of nodes to be explored, m is the number of edges, and 2<γ<3 is a constant.