Many link prediction methods only focus on the standard metric AUC (Area Under receiver operating characteristic Curve), ignoring the metric precision and closeness of common neighbors and endpoints under different topological structures. To solve these problems, a link prediction method based on closeness between nodes was proposed. In order to describe the similarity between endpoints more accurately, the closeness of common neighbors was designed by considering the local topological information around common neighbors, which was adjusted for different networks through a parameter. Empirical study on six real networks show that compared with the similarity indicators such as Common Neighbor (CN), Resource Allocation (RA), Adamic-Adar (AA), Local Path (LP) and Katz, the proposed index can improve the prediction accuracy.