In　the　context　of　high-quality　economic　development,　technological　innovation　has　emerged　as　a　fundamental　driver　of　socio-economic　progress.　The　consequent　proliferation　of　science　and　technology　news,　which　acts　as　a　vital　medium　for　disseminating　technological　advancements　and　policy　changes,　has　attracted　considerable　attention　from　technology　management　agencies　and　innovation　organizations.　Nevertheless,　online　science　and　technology　news　has　historically　exhibited　characteristics　such　as　limited　scale,　disorderliness,　and　multi-dimensionality,　which　is　extremely　inconvenient　for　users　of　deep　application.　While　single-label　classification　techniques　can　effectively　categorize　textual　information,　they　face　challenges　in　leading　science　and　technology　news　classification　due　to　a　lack　of　a　hierarchical　knowledge　framework　and　insufficient　capacity　to　reveal　knowledge　integration　features.　This　study　proposes　a　hierarchical　multi-label　classification　model　for　science　and　technology　news,　enhanced　by　heterogeneous　graph　semantics.　The　model　captures　multi-dimensional　themes　and　hierarchical　structural　features　within　science　and　technology　news　through　a　hierarchical　transmission　module.　It　integrates　graph　convolutional　networks　to　extract　node　information　and　hierarchical　relationships　from　heterogeneous　graphs,　while　also　incorporating　prior　knowledge　from　domain　knowledge　graphs　to　address　data　scarcity.　This　approach　enhances　the　understanding　and　classification　capabilities　of　the　semantics　of　science　and　technology　news.　Experimental　results　demonstrate　that　the　model　achieves　precision,　recall,　and　F1　scores　of　84.21%,　88.89%,　and　86.49%,　respectively,　significantly　surpassing　baseline　models.　This　research　presents　an　innovative　solution　for　hierarchical　multi-label　classification　tasks,　demonstrating　significant　application　potential　in　addressing　data　scarcity　and　complex　thematic　classification　challenges.