Magnetic　materials　are　important　basic　materials　in　the　information　age.　Different　magnetic　ground　states　are　the　prerequisite　for　the　wide　application　of　magnetic　materials,　among　which　the　ferromagnetic　ground　state　is　a　key　requirement　for　future　high-performance　magnetic　materials.　In　this　paper,　machine　learning　is　used　to　study　the　classification　of　ferromagnetic,　antiferromagnetic,　ferrimagnetic　and　paramagnetic　ground　states　of　inorganic　magnetic　materials　and　the　prediction　of　magnetic　moments　of　inorganic　ferromagnetic　materials.　Weobtain　98888　inorganic　magnetic　materials　data　from　the　Materials　Project　database,　containing　material　ids,chemical　formulae,　CIF　files,　magnetic　ground　states　and　magnetic　moments,　and　extract　582　elemental　and　structural　features　for　the　inorganic　magnetic　materials　by　using　Matminer.　We　design　a　two-step　feature　selection　method.　In　the　first　step,　RFECV　is　used　to　evaluate　material　features　one　by　one　to　removeredundant　features　without　degrading　the　model　accuracy.　In　the　second　step,　we　rank　the　material　features　to　further　refine　and　select　the　most　important　material　features　for　the　model,　and　20　material　features　are　selected　for　the　classification　of　magnetic　ground　states　and　the　prediction　of　magnetic　moments,　respectively.Among　the　selected　material　features,　it　is　found　that　the　electronegativity,　the　atomic　own　magnetic　momentand　the　number　of　unfilled　electrons　in　the　atomic　peripheral　orbitals　all　make　important　contributions　to　theclassification　of　magnetic　ground　states　and　the　prediction　of　magnetic　moments.　We　build　a　magnetic　groundstate　classification　model　and　a　magnetic　moment　prediction　model　by　using　the　random　forest,　andquantitatively　evaluate　the　machine　learning　models　by　using　the　10-fold　cross-validation　approach,　and　the　results　show　that　the　constructed　machine　learning　models　has　sufficient　accuracy　and　generalization　capability　.In　the　test　set,　the　magnetic　ground　state　classification　model　has　an　accuracy　of　85.23%,　a　precision　of　85.18%,a　recall　of　85.04%,　and　an　F1　score　of　85.24%;　the　magnetic　moment　prediction　model　has　a　goodness-of-fit　of91.58%　and　an　average　absolute　error　of　0.098　mu　B　per　atom.　This　study　provides　a　new　method　and　choice　for　high-throughput　classification　and　screening　of　magnetic　ground　states　of　inorganic　magnetic　materials　and　predicting　the　magnetic　moment　of　ferromagnetic　materials