Adopting　deep　learning　methods　to　classify　functional　connectivity　(FC)　patterns　for　brain　disease　diagnosis　has　become　a　new　research　hotspot.　Recently,　deep　neural　network　(DNN)　with　fully　connected　structure　has　been　widely　applied.　However,　the　dense　connections　between　adjacent　layers　bring　abundant　trainable　parameters.　Moreover,　the　current　DNN　models　only　extract　FC　features　along　one　dimension,　which　lack　potential　multi-channel　descriptions　for　the　FC　features.　With　the　rapid　development　of　convolutional　neural　network　(CNN),　various　CNN-based　deep　learning　models　have　been　applied　in　pattern　recognition.　In　this　study,　we　proposed　a　sparse　multi-scale　CNN　(SMS-CNN)　model　to　classify　FC　patterns　for　brain　disease　diagnosis.　In　our　model,　multi-scale　convolution　operations　were　conducted　to　aggregate　the　FC　features　from　different　scales　of　spatial　neighborhoods.　In　addition,　to　further　alleviate　over-fitting,　we　designed　a　lightweight　feature　sparse　layer　to　scale　the　redundant　FC　features　and　increase　the　sparsity　of　the　model.　We　conducted　systematic　experiments　on　the　large-scale　Autism　Brain　Imaging　Data　Exchange　(ABIDE)　dataset　to　validate　the　classification　performance　of　the　proposed　model.　Experimental　results　showed　that　the　proposed　SMS-CNN　can　better　learning　and　classifying　FC　patterns　and　achieved　high　classification　performance　in　distinguishing　autism　patients　from　healthy　controls.　Our　study　indicates　the　potential　of　CNN-based　models　in　FC　pattern　analysis,　and　provides　a　promising　method　to　further　improve　the　classification　performance　for　FC-based　brain　disease　diagnosis.　©　2022　IEEE.