Brain　disorders　such　as　autism　spectrum　disorder　(ASD)　is　still　difficult　to　diagnose.　In　the　recent　years,　different　novel　deep　learning　algorithms　have　been　applied　to　detect　ASD.　Most　studies　use　the　functional　connectivity　(FC)　pattern　to　represent　the　brain　activities.　However,　it　has　been　investigated　that　dynamic　functional　connectivity　(dFC)　which　represent　more　features　than　FC　can　characterize　the　intrinsic　brain　organization　changes　over　time.　The　goal　of　this　paper　is　to　determine　that　dFC　features　are　more　successful　than　FC　features　in　the　classification　of　ASD　using　deep　learning.　In　this　paper,　we　propose　a　classification　model　using　dFC　and　deep　neural　network.　Firstly,　we　used　windowed　k-means　(WKM)　approach　to　compute　the　sub-state　of　the　brain　and　extract　the　main　features　of　the　functional　magnetic　resonance　imaging(fMRI).　Then,　two　stacked　denoising　autoencoders　were　applied　to　extract　the　features　and　reduce　the　dimension.　At　last,　the　MLP　was　utilized　to　complete　the　classification　task　and　do　fine-tuning　based　on　the　autoencoder　encoders　weights.　The　experiments　were　carried　out　on　the　Autism　Brain　Imaging　Data　Exchange　(ABIDE)　datasets.　Result　shows　that　we　acquired　a　mean　accuracy　of　68.51%.　Overall,　our　proposed　classification　is　effective　and　provide　evidence　that　dFC　contains　more　brain　states　features.　　©　2022　ACM.