As　a　challenging　task　of　Natural　Language　Processing(NLP),　zero-shot　text　classification　has　attracted　more　and　more　attention　recently.　It　aims　to　detect　classes　that　the　model　has　never　seen　in　the　training　set.　For　this　purpose,　a　feasible　way　is　to　construct　connection　between　the　seen　and　unseen　classes　by　semantic　extension　and　classify　the　unseen　classes　by　information　propagation　over　the　connection.　Although　many　related　zero-shot　text　classification　methods　have　been　exploited,　how　to　realize　semantic　extension　properly　and　propagate　information　effectively　are　far　from　solved.　In　this　paper,　we　propose　a　novel　zero-shot　text　classification　method　called　Semantically　Extended　Graph　Convolutional　Network　(SEGCN).　In　the　proposed　method,　the　semantic　category　knowledge　from　ConceptNet　is　utilized　to　semantic　extension　for　linking　seen　classes　to　unseen　classes　and　constructing　a　graph　of　all　categories.　Then,　we　build　upon　Graph　Convolutional　Network　(GCN)　for　predicting　the　textual　classifier　for　each　category,　which　transfers　the　category　knowledge　by　the　convolution　operators　on　the　constructed　graph　and　is　trained　in　a　semi-supervised　manner　using　the　samples　of　the　seen　classes.　The　experimental　results　on　Dbpedia　and　20newsgroup　datasets　show　that　our　method　outperforms　the　state　of　the　art　zero-shot　text　classification　methods.