Focused on the issue that large-scale network has characteristics of huge number of nodes, high structural complexity and difficulty to demonstrate its structural characteristics by the limited screen space, a multi-level network visualization method based on community detection was proposed. Firstly, a community detection algorithm based on network modularity was used to detect the network node and a greedy algorithm was used to find the community detection with maximum modularity to get different level of granularity communities. Then, in order to solve the problem that the Force-Directed Algorithm (FDA) could not display network nodes hierarchically, the classic FDA was improved by setting the level blinding force to achieve hierarchical layout of different level of granularity communities. Finally, high level communities and low level nodes were displayed respectively by using the interactive method such as multi-window view and Overview+Detail, meeting the requirement of both network high-level macrostructure and low-level details of the display. In the simulation test, the community detection algorithm is faster and more accurate compared to self-contained GN (Girvan-Newman) algorithm. The theoretical analysis and simulation results show that the proposed method has good effect and performance in display and interaction of large-scale network structure.