<p>Multi-source information fusion is a key technique in big data technology, essential for data mining and knowledge discovery. When merging traditional multi-source systems into single systems, data loss is a common issue. Multi-granularity information fusion methods are designed to solve this problem. However, there is limited research on how to use these methods dynamically to extract valuable information when there are changes in three dimensions: objects, information sources, and the number of attributes. Additionally, information fusion in multi-source fuzzy information systems can better handle uncertainties in data. This paper proposes a method for calculating multi-granularity fusion operators based on a similarity relationship matrix and creates an incremental update fusion mechanism for six different scenarios, such as adding or removing objects, information sources, and attributes. Experimental results on 12 public datasets show that our proposed dynamic fusion method has clear advantages in dealing with complex changes and can update the fusion operator more efficiently.</p>