Concerning the increasingly high real-time processing requirement of space science satellite observed data, a real-time processing method of space science satellite data based on stream computing framework was proposed. Firstly, the data stream was abstractly analyzed according to the data processing characteristics of space science satellite. Then, the input and output data structures of each processing unit were redefined. Finally, the parallel data stream processing structure was designed based on the stream computing framework Storm to meet the requirements of parallel processing and distributed computing of large-scale data. The developed system for space science satellite data processing applying with this method was tested and analyzed. The results show that the data processing time is half of that of the original system under same conditions and the data localization strategy has higher throughput than round-robin strategy with the data tuple throughput increased by 29% on average. It can be seen that the use of stream computing framework can greatly shorten the data processing delay and improve the real-time performance of the space science satellite data processing system.