The community structure exists generally in the complex network, so the community detection has important theoretical significance and practical value. In order to improve the performance of community detection in the complex network, a community detection algorithm based on structural similarity affinity propagation was proposed. Firstly, the algorithm selected structural similarity as a similarity measurement between nodes, and applied an optimized method to calculate the similarity matrix of complex networks. Secondly, the algorithm made the similarity matrix as an input, and used a Fast Affinity Propagation (FAP) algorithm to cluster. Finally, the algorithm got the final community structure. The experimental results show that in the LFR (Lancichinetti-Fortunato-Radicchi) simulated network, the average community detection Normalized Mutual Information (NMI) value of the proposed algorithm is 65.1%, which is higher than 45.3% of the Label Propagation Algorithm (LPA) and 49.8% of CNM (Clauset-Newman-Moore) algorithm. And in the real network, the average community detection modularity value of the proposed algorithm is 53.1%, which is also higher than 39.9% of the LPA and 47.8% of the CNM algorithm. The proposed algorithm has better ability of community detection, but also can find a higher quality of community structure.