Aiming at the problem of scattered initial labels and large randomness of label propagation, an overlapping community detection algorithm fusing label preprocessing and node influence was proposed. Firstly, the influence value of each node was calculated, and the node with the largest influence value was selected as the central node gradually. Secondly, the label of the central node was used to preprocess the labels of the homogeneous neighbor nodes, so as to reduce the number of initial labels as well as the randomness of subsequent label propagation, and preliminarily identify the overlapping nodes. Thirdly, the overlapping nodes were identified by the label belonging coefficient, and the labels of non-overlapping nodes were selected by the node influence values, improving the stability and accuracy of the proposed algorithm. Finally, in order to maximize the increment of the adaptive function, the communities with weak cohesion were merged together to improve the quality of communities. The simulation experimental results show that the proposed algorithm has the largest extended modularity value on 50% datasets of the six real networks, and has the best performance in Normalized Mutual Information (NMI) index on the artificial benchmark networks with different mixing degrees, overlapping degrees of node and the maximum numbers of communities to which the node belongs. In conclusion, the algorithm has good adaptability to all kinds of networks, and has nearly linear time complexity.