As wireless network has developed rapidly in recent years, especially with the maturity and wide application of 5G wireless system, millions of mobile users have been able to quickly exchange large amounts of data in social networks. Despite the positive impact of the recent advances in edge computing on opportunistic social networks (OSNs), classical OSN algorithms rely on only a few source nodes to forward data, which often results in data transmission delay, excessive resource consumption and even loss of source nodes. Therefore, we propose an efficient edge-computing-based data transmission strategy in OSNs. It classifies nodes into new communities according to their degree of association. Source nodes forward data through relay nodes in community, which effectively reduces the resource consumption of a single node. The experiment results of comparison with the three algorithms show that the proposed method can effectively reduce the resource consumption during data transmission as well as enhance the information transmission efficiency.