In　order　to　improve　the　performance　of　time　series　similarity　measure,　a　model　combined　Siamese　and　Sequential　Neural　Network(SSNN)　is　proposed.　The　model　consists　of　three　parts:　siamese　neural　network,　distance　measurement　and　sequential　neural　network.　Time　series＇　features　are　extracted　through　the　siamese　network,　the　distance　measurement　calculates　the　sequence　distance　in　the　feature　space,　and　the　sequential　network　calculates　the　similarity　between　two　time　series　according　to　the　result　of　distance　mesurement.　The　siamese　neural　network　and　the　sequential　neural　network　are　composed　of　multilayer　perceptrons,　and　the　distance　measurement　calculates　the　Euclidean　distance　between　the　corresponding　dimensions　of　the　time　series.　Experimental　results　on　22　time　series　datasets　in　UCRArchive2018　show　that　SSNN　has　better　classification　performance　than　several　other　methods,　and　has　a　good　ability　to　measure　similarity.