Different　from　the　classical　k-means　problem,　the　functional　k　means　problem　involves　a　kind　of　dynamic　data,　which　is　generated　by　continuous　processes.　In　this　paper,　we　mainly　design　an　O(ln　k)-approximation　algorithm　based　on　the　seeding　method　for　functional　k-means　problem.　Moreover,　the　numerical　experiment　presented　shows　that　this　algorithm　is　more　efficient　than　the　functional　k-means　clustering　algorithm.