The　analysis　of　functional　connectivity　(FC)　has　become　the　major　method　in　recent　functional　magnetic　resonance　imaging　(fMRI)　research　for　brain　disease　diagnosis.　Most　of　the　present　FC　classification　methods　were　established　based　on　the　static　FC　patterns.　However,　an　increasing　number　of　studies　have　shown　that　dynamic　FC　(DFC)　patterns　contain　abundant　spatial　and　temporal　information,　which　may　further　promote　the　classification.　In　this　study,　we　constructed　the　DFC　patterns　and　proposed　a　novel　DFC　spatial-temporal　integration　analysis　(DFC-ST)　to　classify　DFC　patterns　for　the　brain　disease　diagnosis.　This　model　extracted　the　abstract　spatial　FC　features　and　retained　the　time　dependence　of　DFC　by　adopting　a　two-stage　configuration,　which　separately　centered　on　the　spatial　and　temporal　property.　In　the　spatial　analysis　stage,　we　designed　multiple　feature　extractors　based　on　autoencoders　to　extract　the　abstract　feature　representations　for　the　FC　patterns　at　each　time　point.　In　the　temporal　analysis　stage,　the　learnt　abstract　features　were　gathered　chronologically,　and　a　separate　deep　neural　network　(DNN)　was　trained　to　classify　the　fused　features　and　obtain　the　prediction　labels.　To　validate　the　model　performance,　we　conducted　experiments　on　the　Autism　Brain　Imaging　Data　Exchange　(ABIDE)　dataset.　Results　demonstrate　that　the　proposed　model　can　more　accurately　distinguish　the　autism　groups　from　healthy　controls　and　the　fused　DFC　features　contain　more　decoding　information.　Our　study　provides　an　effective　approach　to　analyze　and　classify　the　DFC　patterns　and　further　promote　the　classification　performance　for　the　FC-based　brain　diseases　diagnosis.