Abstract: The typical algorithm of text clustering is a“Hard Partition” one.Actually，Chinese text is better to treat with “Soft Partition” for its diversity and largeness.The fuzzy-set theory supplies a powerful analyzing tool to this “Soft partition”.Traditional fuzzy text clustering methods mostly get the fuzzy equivalent matrix or fuzzy division by iterating the matrix of membership degree.Huge storage space is necessary for that process.The text clustering based on fuzzy granular computing will work as：First a normalized distance function d（d_i，d_j） in the fuzzy granularity space of text set is provided，and then the function is used to do a dynamic clustering work to text who has a less distance than granularity d_λ.Approved by the test，this method has such advantages on reducing the computing complexity and space complexity，suitable for the status that many samples need to be processed.