Traditional　machine　learning　techniques,　including　support　vector　machine　(SVM),　random　walk,　and　so　on,　have　been　applied　in　various　tasks　of　text　sentiment　analysis,　which　makes　poor　generalization　ability　in　terms　of　complex　classification　problem.　In　recent　years,　deep　learning　has　made　a　breakthrough　in　the　research　of　Natural　Language　Processing.　Convolutional　neural　network　(CNN)　and　recurrent　neural　networks　(RNNs)　are　two　mainstream　methods　of　deep　learning　in　document　and　sentence　modeling.　In　this　paper,　a　model　of　capturing　deep　sentiment　representation　based　on　CNN　and　long　short-term　memory　recurrent　neural　network　(LSTM)　is　proposed.　The　model　uses　the　pre-trained　word　vectors　as　input　and　employs　CNN　to　gain　significant　local　features　of　the　text,　then　features　are　fed　to　two-layer　LSTMs,　which　can　extract　context-dependent　features　and　generate　sentence　representation　for　sentiment　classification.　We　evaluate　the　proposed　model　by　conducting　a　series　of　experiments　on　dataset.　The　experimental　results　show　that　the　model　we　designed　outperforms　existing　CNN,　LSTM,　CNN-LSTM　(our　implement　of　one-layer　LSTM　directly　stacked　on　one-layer　CNN)　and　SVM　(support　vector　machine).