Aiming at the problem of weighted overlapping community detection in complex network, the DBGLLJ (modularity Density and Jaccard based BGLL) method for weighted network is proposed. The network is firstly reconstructed by the importance of node, and then the network is divided into a series of segment according to the modularity gain and the module density gain as the phase function. The overlapping detection method combined with the improved Jaccard index is also proposed. In order to verify the proposed method, three algorithms were selected for testing in LFR networks and real-life networks. The results show that DBGLLJ method is better than the others in standard LFR networks and real-life networks, and has higher overlapping modularity which shows the effectiveness and accuracy of the proposed method. The proposed method is also applied to the reality network of the complex electromechanical system. The overlapping detection result is better and has higher reference value.