The　existing　deep　learning　method　is　insufficient　to　extract　features　in　the　text　sentiment　classification　task.　To　solve　the　drawback,　a　text　sentiment　classification　algorithm　based　on　the　double　channel　convolutional　neural　network　with　extended　features　and　a　dynamic　pooling　is　presented.　Firstly,　various　word　features　influencing　the　sentiment　orientation　of　text,　such　as　emotional　word,　part　of　speech,　adverb　of　degree,　negative　word　and　punctuation,　are　combined　to　obtain　an　extended　text　feature.　Then,　the　word　vector　feature　and　the　extended　text　feature　are　used　as　two　individual　channels　of　the　convolutional　neural　network,　and　a　new　dynamic　k-max　pooling　strategy　is　adopted　to　improve　the　efficiency　of　feature　extraction.　The　experimental　results　on　standard　English　datasets　demonstrate　that　the　proposed　algorithm　achieves　better　classification　efficiency　than　traditional　convolutional　neural　network　algorithm　with　single　channel,　and　it　is　more　advantageous　compared　with　some　elitist　text　sentiment　classification　algorithms.　©　2018,　Science　Press.　All　right　reserved.