Since the amount of information on today’s computers grows, helping computer users to locate the required files in file systems has become an important topic in today’s intelligent interaction model research. Past research has mostly concentrated in PIM (personal information management), to re-organize file hierarchies in a more understandable way for individual users. However, due to the numerous extra operations and the long period that needed for the re-organizing of users’ knowledge systems, the preceding applications can hardly be adopted by users. Considering that there would be a certain topic or purpose when user accessing files (a user may always view several files that related to the same topic during the same time), this paper proposes file recommendation based on tracking user’s file operations. An intelligent file recommendation desktop toolkit (IFRDT) is implemented, which will track user’s file access history, recommend the most related files according to the currently being accessed file, to reduce time cost for finding desired information. Experimental results show that IFRDT can save more energy of searching files than history, and users can find over 50% of desired files in IFRDT and directly open them without searching in directories.