Chronic　disorders　of　consciousness　(DOC)　refers　to　brain　damage　caused　by　various　reasons,　resulting　in　the　reduction　or　loss　of　patients＇　ability　to　perceive　the　stimuli　from　the　environment　and　themselves.　DOC　includes　vegetative　state　/　unresponsive　wakefulness　syndrome　(VS/UWS)　and　minimally　conscious　state　(MCS).　Many　researchers　have　done　a　lot　of　research　on　the　automatic　classification　of　VS　and　MCS　patients.　In　this　study,　we　proposed　an　automatic　state　classification　method　based　on　machine　learning.　Firstly,　the　EEG　signal　is　extracted　by　feature　measurement　methods　such　as　time　domain,　frequency　domain,　time-frequency　domain,　and　nonlinear　analysis,　and　a　total　of　34　kinds　of　the　abovementioned　features　are　extracted.　Then　an　eXtreme　Gradient　Boosting　(XGBoost)　classifier　is　established　based　on　the　extracted　feature　vectors　and　applied　to　the　collected　dataset　for　state　classification.　The　data　set　in　this　paper　uses　the　EEG　data　of　12　patients　(including　DOC　and　normal　state)　collected　by　Fujian　Sanbo　Funeng　Brain　Hospital　for　experiments　to　verify　the　feasibility　and　effectiveness　of　the　proposed　method.　The　experimental　results　show　that　the　classification　accuracy　of　the　proposed　method　for　VS,　MCS,　and　Normal　state　patients　is　99.91%.　©　2022　IEEE.