The　mechanism　of　how　protein　amino　acid　sequences　determine　protein　structure　is　a　core　issue　in　biology.　The　protein　fold　type　reflects　the　topological　pattern　of　the　structure＇s　core.　Fold　recognition　is　an　important　method　in　protein　sequence-structure　research.　This　article　focuses　on　the　36　fold　types　that　are　not　incorporated　into　the　unified　hidden　Markov　model　(HMM)　model　but　that　account　for　41.8%　of　alpha,　beta,　and　alpha/beta　protein＇s　in　the　Astral　1.65　sequence　database.　The　training　set　contains　samples　that　have　less　than　25%　sequence　identity　with　each　other.　We　applied　the　hierarchical　clustering　method　according　to　root　mean　square　deviation　(RMSD)　and　fold　subgroups　were　generated.　A　profile-HMM　based　on　a　multiple　structural　alignment　algorithm　(MUSTANG)　structure　alignment　was　then　built　for　each　subgroup.　After　testing　9505　proteins　with　less　than　95%　sequence　identity　from　the　Astral　1.65　database,　the　average　sensitivity,　specificity　and　Matthew＇s　correlation　coefficient　(MCC)　of　the　36　fold　types　were　found　to　be　90%,　99%　and　0.95,　respectively.　These　results　show　that　classification　modeling　according　to　RMSD　is　able　to　achieve　precise　fold　recognition　while　a　unified　HMM　cannot　be　built　because　there　are　too　many　elements　in　the　training　set.　We　have　developed　a　new　method　and　novel　ideas　to　enable　profile-HMM　protein　fold　recognition　and　have　laid　the　foundation　for　further　research.