Many network structure information-based link prediction algorithms estimate the similarity between nodes and perform link prediction by using the clustering degree of nodes. However, these algorithms only focus on the clustering coefficient of nodes in network, and do not consider the influence of link clustering coefficient between the predicted nodes and their common neighbor nodes on the similarity between nodes. Aiming at the problem, a link prediction algorithm combining node clustering coefficient and asymmetric link clustering coefficient was proposed. Firstly, the clustering coefficient of common neighbor node was calculated, and the average link clustering coefficient of the predicted nodes was obtained by using two asymmetric link clustering coefficients of common neighbor node. Then, a comprehensive measurement index was obtained by fusing these two clustering coefficients based on Dempster-Shafer(DS) theory, and by applying the index to Intermediate Probability Model (IMP), a new node similarity index, named IMP_DS, was designed. The experimental results on the data of nine networks show that the proposed algorithm achieves performance in terms of Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) and Precision in comparison with Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA) indexes and InterMediate Probability model based on Common Neighbor (IMP_CN).