Most community detection algorithms in complex networks find communities based on topological structure of the network. Some important information is included in real network data, which represents data reliability or link closeness. Combined these prior information to detect communities might obtain better clustering results. An overlapping community detection on weighted networks (OCDW) is proposed in this study. Edge weight is defined by combining network topological structure and real information. Then, vertex weight is induced by edge weight. To obtain cluster, OCDW selects seed nodes according to vertex weight. After finding a cluster, edges in this cluster reduce their weights to avoid being selected as a seed node with high probability. Compared with some classical algorithms on 9 real networks including 5 unweighted networks and 4 weighted networks, OCDW shows a considerable or better performance on F-measure, accuracy, separation, NMI, ARI, modularity and time efficiency.