Link prediction is an important research direction of complex networks,and the method based on the node similarity is one of the most popular methods. So far, most of the node similarity prediction methods using link density have not considered the difference of each common neighbor node, that is, the contribution of different nodes to the link is different. Therefore, this paper proposes a link prediction algorithm based on the node contribution and link density of the common neighbor nodes(LDNC). The algorithm first calculates the link information between the common neighbor nodes as the link density of the nodes, and then defines node-coupling clustering coefficient to describe the contribution of the common neighbor nodes, and finally combines the two parameters.Experiments based on the real-world datasets show that the LDNC is more accurate compared with four baseline link prediction algorithms (CN,AA,RA and Jaccard) and the CNBIDE algorithm based on the node link density.