The　dynamic　nature　of　the　brain　functional　connectivity　(FC)　is　well　accepted　in　recent　years.　However,　most　of　the　current　FC　classification　methods　are　based　on　the　static　estimation　of　FC.　In　this　paper,　we　propose　a　novel　convolutional　neural　network　with　an　element-wise　filter　for　classifying　dynamic　functional　connectivity　(DFC-CNN).　First,　a　DFC　matrix　is　estimated　to　quantify　the　DFC.　Then,　taking　the　DFC　matrix　as　input,　the　DFC-CNN　model　employs　one-dimensional　convolutional　kernels　to　extract　the　high-level　features　of　DFC.　Moreover,　an　element-wise　filter　is　specially　designed　for　the　DFC　matrix,　which　further　improves　the　classification　performance.　The　experimental　results　on　the　autism　brain　imaging　data　exchange　I　(ABIDE　I)　indicate　that　the　proposed　model　can　distinguish　subject　groups　more　accurately,　and　also　can　be　used　to　identify　the　abnormal　brain　regions.