Automated　recognition　of　pneumonia　from　chest　CT　plays　an　important　role　in　the　subsequent　clinical　treatment　for　patients.　While　a　few　pioneering　works　only　focus　on　several　random　slices　from　chest　CT　image,　thus　they　have　ignored　the　anatomical　dependency　information　of　local　lesions.　Considering　it,　this　paper　explores　a　novel　automatic　classification　method　for　pneumonia　detection　based　on　fusing　regional　and　global　information,　which　not　only　improves　detection　performance,　but　also　provides　explainable　diagnostic　basis　for　radiologists.　Firstly,　identifying　the　interested　local　region　by　a　lesion　detection　module,　then　we　extracts　the　correlation　relationship　between　local　regions　through　a　graph　attention　module.　The　image-level　classification　results　can　be　acquired　by　fusing　the　information　of　global　and　local　region.　To　realize　the　detection　of　full　CT　sequence,　a　person-level　classifier　is　designed　in　the　proposed　model.　In　the　experiment,　we　collected　781　chest　CT　sequences　in　total　corresponding　to　274　cases　of　viral　pneumonia　patients,　285　cases　of　bacterial　pneumonia　patients　and　222　cases　of　healthy　people.　The　experimental　results　show　that　our　model　achieves　the　accuracy　of　95.5%,　with　95.6%　precision　and　0.991　AUC.　The　recall　and　F1　score　are　95.8%　and　95.7%　respectively,　which　outperformed　previous　works.　Therefore,　our　method　can　be　regarded　as　an　efficient　assisted　tool　in　the　diagnose　of　pneumonia.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd　2024.