Text　classification　is　an　important　task　in　natural　language　processing,　where　text　is　classified　into　different　categories　by　labelling.　However,　due　to　the　complex　structure　and　deep　semantics　of　natural　language,　it　is　still　challenging　to　deeply　analyze　the　intrinsic　structure　of　text　and　extract　text　features.　In　this　paper,　we　adopt　a　parallel　combination　of　local　and　global　features　to　extract　potential　semantic　information　from　text　data.　Among　them,　global　features　are　obtained　by　the　Context-BiLSTM　method.　The　method　fuses　the　hidden　states　in　both　the　forward　and　backward　directions　through　a　gate　mechanism　before　entering　the　cell,　instead　of　simply　summing　the　forward　and　backward　hidden　states　at　the　end.　The　local　features　are　obtained　through　the　Pooling　Word　Attention　CNN(PWACNN)　model.　This　method　generates　the　pooling　results　with　embedding　participation,　so　the　pooling　results　can　focus　on　its　relevant　features　in　the　original　embedding.　Finally,　the　PWACNN　and　Context-BiLSTM　fusion　model　into　the　softmax　layer　for　classification.　Experimental　results　show　that　the　proposed　model　performs　well　and　can　improve　the　accuracy　of　classification　significantly.　©2023　IEEE.