Dynamic　functional　connectivity　(DFC)　classification　is　helpful　for　computer-aided　diagnosis　of　brain　diseases.　In　recent　years,　DFC　classification　based　on　deep　learning　has　drawed　increasing　attention.　However,　how　to　effectively　extract　the　deep　spatio-temporal　features　of　DFC　to　improve　classification　performance　is　still　a　very　challenging　research　topic.　To　this　end,　this　paper　proposes　a　DFC　classification　method　based　on　convolutional　bidirectional　gated　recurrent　unit,　called　DFC-CBGRU,　which　mainly　includes　three　key　operations:　multi-scale　topological　features　extraction,　bidirectional　spatio-temporal　feature　extraction,　and　feature　fusion.　Firstly,　the　proposed　method　uses　convolutional　neural　network　(CNN)　to　extract　the　multi-scale　topological　features　composed　of　node-level,　module-level,　and　graph-level　features　from　functional　connectivity　network　at　each　time　point.　Then,　it　employs　bidirectional　gated　recurrent　unit　(GRU)　to　extract　the　bidirectionally　dependent　spatio-temporal　features　from　the　obtained　time　series　of　multi-scale　topological　features.　Finally,　it　utilizes　the　one-dimensional　CNN　to　fuse　forward　and　backward　spatio-temporal　features　to　obtain　the　joint　spatiotemporal　features　for　classification.　Experimental　results　on　multiple　brain　diseases　datasets　show　that　the　proposed　method　has　a　superior　classification　performance　over　other　methods　and　is　promising　for　extracting　the　discriminative　FCs　related　to　brain　diseases　accurately.