In　the　era　of　big　data,　many　sparse　linear　discriminant　analysis　methods　have　been　proposed　for　classification　and　variable　selection　of　the　high-dimensional　data.　In　order　to　solve　the　multiclass　sparse　discriminant　problem　for　high-dimensional　data　under　the　Gaussian　graphical　model,　this　paper　proposes　a　multiclass　sparse　discrimination　analysis　method　by　incorporating　the　graphical　structure　among　predictors,　which　is　named　as　IG-MSDA　method.　Our　proposed　IG-MSDA　method　can　be　used　to　estimate　the　vectors　of　all　discriminant　directions　simultaneously.　Under　certain　regularity　conditions,　it　is　shown　that　the　proposed　IG-MSDA　method　can　consistently　estimate　all　discriminant　directions　and　the　Bayes　rule.　Further,　we　establish　the　convergence　rates　of　the　estimators　for　the　discriminant　directions　and　the　conditional　misclassification　rates.　Finally,　simulation　studies　and　a　real　data　analysis　demonstrate　the　good　performance　of　our　proposed　IG-MSDA　method.