Whole　brain　functional　connectivity　(FC)　patterns　obtained　from　resting-state　functional　magnetic　resonance　imaging　(rs-fMRI)　have　been　widely　used　in　the　diagnosis　of　brain　disorders　such　as　autism　spectrum　disorder　(ASD).　Recently,　an　increasing　number　of　studies　have　focused　on　employing　deep　learning　techniques　to　analyze　FC　patterns　for　brain　disease　classification.　However,　the　high　dimensionality　of　the　FC　features　and　the　interpretation　of　deep　learning　results　are　issues　that　need　to　be　addressed　in　the　FC-based　brain　disease　classification.　In　this　paper,　we　proposed　a　multi-scale　attention-based　deep　neural　network　(MSA-DNN)　model　to　classify　FC　patterns　for　the　ASD　diagnosis.　The　model　was　implemented　by　adding　a　flexible　multi-scale　attention　(MSA)　module　to　the　auto-encoder　based　backbone　DNN,　which　can　extract　multi-scale　features　of　the　FC　patterns　and　change　the　level　of　attention　for　different　FCs　by　continuous　learning.　Our　model　will　reinforce　the　weights　of　important　FC　features　while　suppress　the　unimportant　FCs　to　ensure　the　sparsity　of　the　model　weights　and　enhance　the　model　interpretability.　We　performed　systematic　experiments　on　the　large　multi-sites　ASD　dataset　with　both　ten-fold　and　leaveone-site-out　cross-validations.　Results　showed　that　our　model　outperformed　classical　methods　in　brain　disease　classification　and　revealed　robust　intersite　prediction　performance.　We　also　localized　important　FC　features　and　brain　regions　associated　with　ASD　classification.　Overall,　our　study　further　promotes　the　biomarker　detection　and　computer-aided　classification　for　ASD　diagnosis,　and　the　proposed　MSA　module　is　flexible　and　easy　to　implement　in　other　classification　networks.