Option　is　an　important　financial　derivative.　Accurate　option　pricing　is　essential　to　the　development　of　financial　markets.　For　option　pricing,　existing　time　series　models　and　neural　networks　are　difficult　to　extract　multi-scale　temporal　features　from　option　data,　which　greatly　limits　their　performance.　To　solve　this　problem,　we　propose　a　novel　deep　learning　model　named　as　MRCLSTM-CI.　It　contains　three　modules,　including　Multi-scale　Residual　CNN　module　(MRC),　Long　Short-Term　Memory　neural　network　module　(LSTM)　and　confidence　interval　output　module　(CI).　The　proposed　model　can　effectively　extract　multi-scale　features　from　real　market　option　data,　and　make　interval　prediction　to　provide　more　information　to　the　decision　maker.　In　addition,　the　proposed　model　is　further　improved　using　the　residual　prediction　strategy,　where　the　output　value　is　chosen　as　the　residual　value　between　BS　theory　price　and　actual　market　price.　Experimental　results　show　that　our　model　has　better　prediction　accuracy　than　other　deep　learning　models　and　achieves　the　state-of-the-art　performance.