Multi-source data is a complex data type that integrates multiple information sources or data sets. Its main feature is that different information sources imply different knowledge structures， and represent and describe samples and relationships between samples from different perspectives. How to fuse and integrate multi-source data cooperatively and how to quickly mine the overall decision-making knowledge for users from different viewpoints have become a scientific problem that needs to be solved urgently in the field of data science. Classical rough set theory， multi-granularity method， evidence theory and information entropy are common and effective multi-source information fusion methods， which have been widely concerned and achieved fruitful results. Therefore， this paper summarizes the work of multi-source information fusion based on granular computing， reviews the basic concepts and main research ideas of each information fusion method， and puts forward some problems in the field of multi-source information fusion. The obtained results can provide a theoretical reference for the follow-up research in this field.