Powerlaw distribution is one of the basic laws of social network data. The long tail part of the data with powerlaw distribution has obvious sparsity. Long tail is always a challenge of recommendation systems. The coldstart, sparsity and coverage problems of recommended systems are important research contents. This paper analyzes the powerlaw characteristics of data, based on studying the personalized recommendation methods of social networks, combining the powerlaw distribution characteristics derived from the behaviors of social network users, the maximum likelihood estimation is used to calculate the scale values of powerlaw distribution. The similarity calculation method is improved by combining the powerlaw characteristics, and a hybrid recommended method based on powerlaw, named PowerLawCF (Collaboration Filter) is proposed. Finally, we use Weibo data to make comparative analysis. PowerLawCF enhances the recommended effect significantly, improves the effect of long tail recommendation, and better solves the cold-start, sparsity and coverage problems of recommended systems, which shows that . The proposed method based on powerlaw distribution characteristics plays a better role in recommendation systems.