The　similarity　measurement　of　time　series　is　a　significant　approach　to　mine　the　rich　and　valuable　law　information　hidden　in　the　massive　time　series　data.　As　the　most　advantageous　approach　in　measuring　similarities　of　time　series,　Dynamic　Time　Warping　(DTW)　has　become　one　of　the　hottest　researches　in　the　field　of　data　mining.　However,　the　DTW　algorithm　does　not　satisfy　the　trigonometric　inequality,　its　time　and　space　complexity　are　extremely　high,　how　to　efficiently　realize　the　retrieval　of　similar　sequences　in　large-scale　sequential　sequences　remains　a　challenge.　This　paper　first　introduces　a　novel　extensible　lower　bound　function　(LB-ex),　then　validates　the　effeteness　of　its　lower　bound　tightness　theoretically,　finally　uses　a　bidirectional　processing　strategy　(BPS)　to　reduce　computation　complexity　and　time　consumption　during　the　massive　sequential　data　retrieval,　and　significantly　improves　the　operation　efficiency.　Extensive　experiments　were　conducted　with　public　dataset　to　evaluate　feasibility　and　efficiency　of　the　proposed　approaches.　The　results　show　that　LB-ex　and　BPS　performs　a　more　robust　and　efficient　processing　of　similarity　of　time　series　than　does　traditional　approaches,　reducing　by　about　43%　of　time-consuming.　©　2019　IEEE.