Classification　of　dynamic　functional　connectivity　(DFC)　is　becoming　a　promising　approach　for　diagnosing　various　neurodegenerative　diseases.　However,　the　existing　methods　generally　face　the　problem　of　overfitting.　To　solve　it,　this　paper　proposes　a　convolutional　neural　network　with　three　sparse　strategies　named　SCNN　to　classify　DFC.　Firstly,　an　element-wise　filter　is　designed　to　impose　sparse　constraints　on　the　DFC　matrix　by　replacing　the　redundant　elements　with　zeroes,　where　the　DFC　matrix　is　specially　constructed　to　quantify　the　spatial　and　temporal　variation　of　DFC.　Secondly,　a　1x1　convolutional　filter　is　adopted　to　reduce　the　dimensionality　of　the　sparse　DFC　matrix,　and　remove　meaningless　features　resulted　from　zero　elements　in　the　subsequent　convolution　process.　Finally,　an　extra　sparse　optimization　classifier　is　employed　to　optimize　the　parameters　of　the　above　two　filters,　which　can　effectively　improve　the　ability　of　SCNN　to　extract　discriminative　features.　Experimental　results　on　multiple　resting-state　fMRI　datasets　demonstrate　that　the　proposed　model　provides　a　better　classification　performance　of　DFC　compared　with　several　state-of-the-art　methods,　and　can　identify　the　abnormal　brain　functional　connectivity.