With　aid　of　good　materials　descriptors,　machine　learning　algorithms　are　able　to　accelerate　the　design　of　new　materials　and　uncover　underlying　mechanisms.　In　the　present　study,　we　adopt　machine　learning　methods　to　discover　the　most　important　materials　descriptors　for　properties　of　ferroelectric　materials.　A　regression　study,　in　typical　BaTiO3-based　and　K1/2Na1/2NbO3-based　lead-free　ceramics　and　lead-contained　PbMg1/3Nb2/3TiO3-PbTiO3　ceramics,　screens　out　three　important　materials　descriptors　determining　ferroelectricity　from　46　potential　descriptors.　The　three　descriptors　of　Matyonov-Batsanov　electronegativity,　ratio　of　valence　electron　number　to　nominal　charge　and　core　electron　distance　(Schubert)　are　confirmed　to　be　dominant　as　well　in　classification　of　perovskite　compounds　into　antiferroelectrics　or　not.　The　classification　based　on　these　descriptors　exhibit　an　excellent　accuracy　of　96%,　much　higher　than　that　of　traditional　criterion　(89%)　using　tolerance　factor　and　Pauling　electronegativity.　Furthermore,　we　propose　a　machine　learning　strategy　based　on　our　descriptors　to　predict　the　phase　coexistence.　The　prediction　probability　after　bootstrapping　provides　an　effective　approach　to　distinguish　the　phase　boundaries　and　predict　the　phase　ratio　of　coexisted　phases.　In　all,　we　identified　materials　descriptors　for　ferroelectric　materials,　which　is　helpful　to　reveal　the　structure-property　relationship　of　ferroelectric　materials　and　guide　the　design　of　better　ferroelectricity　and　piezoelectricity.　©　2021