As　convolutional　neural　networks　(CNN)　and　recurrent　neural　networks　(RNN)　have　achieved　excellent　results　in　the　field　of　Chinese　text　sentiment　analysis.　More　and　more　researchers　are　extracting　features　of　text　based　on　the　advantages　of　CNN　and　RNN　in　extracting　features.　However,　the　current　scholars　fail　to　make　full　use　of　sentiment　language　resources　such　as　sentiment　words,　negatives　and　degree　adverbs　when　they　adopt　deep　learning　methods.　For　implicit　texts　without　explicit　sentiment　words,　they　cannot　fully　identify　the　differences　between　words　and　sentiment　tendency.　At　the　same　time,　it　fails　to　consider　the　grammatical　structure　of　the　text,　which　leads　to　poor　classification　effect　for　some　turning　sentences　or　summary　sentences.　In　addition,　most　of　the　models　are　mainly　input　in　the　form　of　word　vector.　For　English,　it　is　very　convenient　to　segment　words　through　spaces　between　words,　but　for　Chinese,　there　may　be　inaccurate　word　segmentation,　which　will　reduce　the　accuracy　of　classification.　To　solve　these　problems,　a　dual　channel　sentiment　classification　model　based　on　grammar　rules　and　multi　attention　(DCGA)　is　proposed.　Firstly,　the　text　with　clear　sentiment　tendency　is　obtained　according　to　the　grammar　rules,　and　the　local　features　of　the　text　are　extracted　by　CNN　channel.　Considering　that　the　grammar　rules　may　ignore　the　context　information,　bi-directional　long　short　term　memory　network　(Bi-LSTM)　channel　is　used　to　extract　the　global　features　containing　the　context　information,　and　attention　pooling　is　used　to　improve　the　sentiment　information　extracted　by　CNN　channel.　Then,　the　part　of　speech　attention　mechanism　is　used　to　mine　the　implicit　sentiment　features　in　the　sentiment　text　to　solve　the　poor　classification　effect　caused　by　the　existence　of　implicit　text.　Finally,　the　local　features　obtained　by　CNN　channel　and　the　global　features　obtained　by　RNN　channel　are　fused　for　classification.　Experimental　results　on　four　Chinese　comment　text　datasets　show　that　the　proposed　model　outperforms　most　existing　methods　in　accuracy.　©　Published　under　licence　by　IOP　Publishing　Ltd.