The　functional　connectivity　provides　new　insights　into　the　mechanisms　of　the　human　brain　at　network-level,　which　has　been　proved　to　be　an　effective　biomarker　for　brain　disease　classification.　Recently,　machine　learning　methods　have　played　an　important　role　in　functional　connectivity　classification,　among　which　convolutional　neural　network　(CNN)　based　methods　become　a　new　hot　topic　since　they　can　extract　topological　features　in　the　brain　network.　However,　the　conventional　CNN-based　methods　haven＇t　taken　sparse　connectivity　patterns　(SCPs)　of　the　human　brain　into　consideration,　which　may　lead　to　redundancy　of　the　topological　features,　and　limit　their　performance　and　generalization.　To　solve　it,　we　propose　a　novel　CNN-based　model　with　graphical　Lasso　(CNNGLasso)　to　extract　sparse　topological　features　for　brain　disease　classification.　First,　we　develop　a　novel　graphical　Lasso　model　for　revealing　the　SCPs　at　group-level.　Then,　the　SCPs　are　used　to　guide　the　topological　feature　extraction.　Finally,　the　obtained　sparse　topological　features　are　used　to　classify　the　patients　from　normal　controls.　The　experiment　results　on　the　ABIDE　dataset　demonstrate　that　the　CNNGLasso　outperforms　the　others　on　various　performances.　Besides,　the　abnormal　brain　regions　derived　from　the　trained　model　are　consistent　with　the　previous　investigations,　which　further　proves　the　application　prospect　of　the　CNNGLasso.