Community detection based on the topological potential constructs the topological potential field by the link information of nodes, in which the community can be partitioned. However, there are a large number of isolated communities in the actual division process. The problem of community discovery with node attribute information, as an important part of the community, has become the main research direction of community discovery. This paper proposes a topological potential community discovery algorithm combined with label propagation (TPCDLP). First, combining the thought of label propagation, the attribute information is converted into the link weights between nodes. Second, the link weights are added to the topological potential to construct the topological potential field. Then, the subgroup communities are partitioned by the core node. Finally, the communities are partitioned by using the distance of the core nodes between the subgroup communities. Compared with six algorithms on three datasets with label attributes, the TPCDLP performs better on the improved modular degree $Q_{ov}^E$, information entropy $Entropy$, community overlap degree $Overlap$ and comprehensive index $F$.