Text　classification　is　an　important　assignment　for　various　text-related　downstream　assignments,　such　as　fake　news　detection,　sentiment　analysis,　and　question　answering.　In　recent　years,　the　graph-based　method　achieves　excellent　results　in　text　classification　tasks.　Instead　of　regarding　a　text　as　a　sequence　structure,　this　method　regards　it　as　a　co-occurrence　set　of　words.　The　task　of　text　classification　is　then　accomplished　by　aggregating　the　data　from　nearby　nodes　using　the　graph　neural　network.　However,　existing　corpus-level　graph　models　are　difficult　to　incorporate　the　local　semantic　information　and　classify　new　coming　texts.　To　address　these　issues,　we　propose　a　Global-Local　Text　Classification　(GLTC)　model,　based　on　the　KL　constraints　to　realize　inductive　learning　for　text　classification.　Firstly,　a　global　structural　feature　extractor　and　a　local　semantic　feature　extractor　are　designed　to　capture　the　structural　and　semantic　information　of　text　comprehensively.　Then,　the　KL　divergence　is　introduced　as　a　regularization　term　in　the　loss　calculation　process,　which　ensures　that　the　global　structural　feature　extractor　can　constrain　the　learning　of　the　local　semantic　feature　extractor　to　achieve　inductive　learning.　The　comprehensive　experiments　on　benchmark　datasets　present　that　GLTC　outperforms　baseline　methods　in　terms　of　accuracy.