Recognition　of　protein　fold　types　is　an　important　step　in　protein　structure　and　function　predictions　and　is　also　an　important　method　in　protein　sequence-structure　research.　Protein　fold　type　reflects　the　topological　pattern　of　the　structure＇s　core.　Now　there　are　three　methods　of　protein　structure　prediction,　comparative　modeling,　fold　recognition　and　de　novo　prediction.　Since　comparative　modeling　is　limited　by　sequence　similarity　and　there　is　too　much　workload　in　de　novo　prediction,　fold　recognition　has　the　greatest　potential.　In　order　to　improve　recognition　accuracy,　a　recognition　method　based　on　functional　domain　composition　is　proposed　in　this　paper.　This　article　focuses　on　the　124　fold　types　which　have　more　than　2　samples　in　LIFCA　database.　We　apply　the　functional　domain　composition　to　predict　the　fold　types　of　a　protein　or　a　domain.　In　order　to　evaluate　our　method　and　its　sensibility　to　the　samples　involving　SCOP　family　divided,　we　tested　our　results　from　different　aspects.　The　average　sensitivity,　specificity　and　Matthew＇s　correlation　coefficient　(MCC)　of　the　124　fold　types　were　found　to　be　94.58%,　99.96%　and　0.91,　respectively.　Our　results　indicate　that　the　functional　domain　composition　method　is　a　very　promising　method　for　protein　fold　recognition.　And　though　based　on　simple　classification　rules,　LIFCA　database　can　grasp　the　functional　features　of　different　proteins,　reflecting　the　corresponding　relation　between　protein　structure　and　function.　(C)　2013　Elsevier　Ltd.　All　rights　reserved.