To select key users with great information dissemination capability efficiently and effectively from large-scale social networks and corresponding historical user massages, an approach for selecting key users was proposed. Firstly, the structure information of the social network was used to construct the directed graph with the user as the node. Based on the Spark calculation framework, the weights of user activity, transmission interaction and information quantity were quantitatively calculated by the historical data of the message, so as to construct a dynamic weighted graph model of social networks. Then, the measurement for user's information dissemination capacity was established based on PageRank and the Spark-based algorithm was given correspondingly for large-scale social networks. Further more, the algorithm for d-distance selection of key users was given to make the overlap of information dissemination ranges of different key users be as less as possible by multiple iterations. The experimental results based on Sina Weibo datasets show that the proposed approach is efficient, feasible and scalable, and can provide underlying techniques to control the spread of bad news and monitor public opinions to a certain extent.