In order to solve the problems of insufficient stability and poor accuracy of label propagation algorithms， a label propagation overlapping community detection algorithm OCKELP （Overlapping Community detection algorithm combining K-shell and label Entropy in Label Propagation） was proposed， which combined K-shell and label entropy. Firstly， the K-shell algorithm was used to reduce the label initialization time， and the update sequence of label entropy was used to improve the stability of the algorithm. Secondly， the comprehensive influence was introduced for label selection， and the community level information and node local information were fused to improve the accuracy of the algorithm. Compared with Community Overlap PRopagation Algorithm （COPRA）， Overlapping community detection in complex networks based on Multi Kernel Label Propagation（OMKLP） and Speaker-listener Label Propagation Algorithm （SLPA）， OCKELP algorithm has the greatest modularity improvement of about 68.64%， 53.99% and 42.29% respectively on the real network datasets. It also has obvious advantages over the other three algorithms in the Normalized Mutual Information （NMI） value of the artificial network datasets， and with the increase of the number of communities to which overlapping nodes belong， the real structures of the communities can also be excavated.