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Link prediction is a fundamental tool for determining relationships between users. Link prediction by similarity measure is a common method. This paper proposes a link prediction algorithm based on similarity, which determines the similarity according to the network structure and topological characteristics, introduces the optimized link prediction measure, and takes the clustering coefficient as the network structure property. In addition, considering the shared neighborhood, the performance is better than other similar link prediction methods. Experimental results show that the proposed algorithm outperforms the classical algorithm. Combined with the experimental results in the social network environment such as Facebook, Twitter and Sina Weibo, it can be seen that SLP-CNP method has better accuracy and efficiency than other algorithms. In the future work, we can also try to improve the applicability of the proposed method in weighted networks, directed networks and bipartite networks.