 This paper presents an optimized method for scheduling big data in social networks, which takes into account conflicting target data in the same data node and each tasks amount of data communication during transmission to construct a big data scheduling model. The model uses a periodic task distribution function and schedules tasks based on minimizing resource level and execution time, resulting in efficient scheduling that quickly optimizes social network data and resolves strong data collision issues. This article was authored by Weina Fu, Shuai Lu and Gautam Srivastava.