Aiming　at　improving　the　generalization　performance　of　the　dynamic　convolutional　neural　network　on　text　sentiment　classification,　a　dynamic　convolutional　extreme　learning　machine　algorithm　was　proposed.　This　algorithm　modified　the　output　layer　of　dynamic　convolutional　neural　network　by　replacing　the　fully　connection　layer　with　the　shallow　random　neural　network.　By　utilizing　the　perturbation　ability　of　the　random　generation　of　parameters,　it　is　prone　to　mitigate　the　dependence　on　training　samples　and　avoid　over-fitting　to　improve　the　classification　performance.　Experiments　on　several　public　data　sets　show　that　this　approach　outperforms　the　dynamic　convolutional　neural　network　and　extreme　learning　machine　under　the　evaluation　metrics　including　accuracy　rate,　F1-measure,　etc.　©　2017,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.