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To analyze user influence based on big data from social network is helpful for recognizing users with good impact on the Internet and realizing their social and economic value. Traditional methods can not process massive social network data efficiently and analyze user influence quantitatively and precisely. To solve these problems, this paper proposed an advanced model of user influence evaluation, originating from classic PageRank algorithm, which took not only user connectivity but activity into consideration, and used Spark GraphX which supported massive parallel computing as a tool and realized analyzing influence of Weibo users quantitatively and precisely. Experiment shows that the approach proposed in this paper is a more efficient method with more precise results. [ABSTRACT FROM AUTHOR]