An academic paper recommendation model based on community partition was proposed according to sociability in social network. The model regarded the largest connected component in complex network as the logic unit in data processing, and divided up the largest connected component into non-intersect kernel sub-network. The labels would be established according to kernel sub-network by non-parameter control mode. Communities were divided in scholar social network through label propagation, and academic papers were recommended among the users in the communities by the results of the community partition. The proposed community partition method was compared with the classic community partition method in the experiments on artificial network. The experimental results show that the proposed method can achieve good community partition qualities on different characteristic artificial networks.