Text　classification　is　a　fundamental　task　in　natural　language　processing.　This　task　is　widely　concerned　and　applied.　However,　previous　methods　mainly　use　traditional　static　word　embedding,　but　static　word　embedding　could　not　deal　with　the　problem　of　polysemy.　For　this　reason,　we　propose　to　utilize　contextualized　BERT　word　embedding　to　effectively　encode　the　input　sequence　and　then　use　the　temporal　convolutional　module　which　simply　computes　a　1-D　convolution　to　extract　high-level　features,　finally,　the　max-pooling　layer　retains　the　most　critical　features　for　text　classification.　We　conduct　experiments　on　six　commonly　used　large-scale　text　categorization　datasets,　including　sentiment　analysis,　problem　classification　and　topic　classification　tasks.　Due　to　the　limitation　of　BERT　processing　long　text,　we　propose　an　effective　truncation　method.　Experimental　results　show　that　our　proposed　method　outperforms　previous　methods.