Shot　boundary　detection　is　essentially　to　detect　the　position　of　frames　where　the　shot　changes.　It　has　been　actively　studied　in　video　analysis　and　management　for　convenience,　which　becomes　a　key　technique　with　the　rapid　proliferation　of　rich　and　diverse　videos.　With　respect　to　the　complex　characteristics　of　different　shots　in　varying　length　and　content　variation　property,　in　this　paper　we　present　a　two　stage　method　for　shot　boundary　detection　(TSSBD)　which　distinguishes　abrupt　shot　by　fusing　color　histogram　and　deep　features,　and　locate　gradual　shot　changes　with　C3D-based　deep　analysis.　Abrupt　shot　changes　are　detected　firstly　as　it　occurs　between　two　frames,　which　divides　the　complete　video　into　segments　containing　gradual　transitions;　Over　these　video　segments,　gradual　shot　change　detection　is　implemented　using　3D-convolutional　neural　network,　which　classifies　clips　into　specific　gradual　shot　change　types;　Finally,　an　effective　merging　strategy　is　proposed　to　locate　positions　of　gradual　shot　transitions.　The　experimental　analysis　illustrates　that　the　proposed　progressive　method　is　capable　of　detecting　both　abrupt　shot　transitions　and　gradual　shot　transitions　accurately.