With　the　continuous　growth　of　the　global　population,　insufficient　food　production　has　become　an　urgent　problem　to　be　solved　in　most　countries.　At　present,　using　artificial　intelligence　technology　to　improve　suitability　between　land　and　crop　varieties　to　increase　crop　yields　has　become　a　consensus　among　agricultural　researchers.　However,　there　are　still　many　problems　in　existing　works,　such　as　limited　crop　phenotypic　data　and　the　poor　performance　of　artificial　intelligence　models.　In　this　regard,　we　take　maize　as　an　example　to　collect　a　large　amount　of　environmental　climate　and　crop　phenotypic　traits　data　at　multiple　experimental　sites　and　construct　an　extensive　dataset.　Then,　we　introduce　a　graph　neural　network　model　to　learn　crop　suitability　evaluation　and　finally　achieve　a　good　evaluation　effect.　The　evaluation　results　of　the　model　can　not　only　provide　a　reference　for　expert　evaluation　but　also　judge　the　suitability　of　the　variety　to　other　test　trial　sites　according　to　the　data　of　the　current　one,　so　as　to　guide　future　breeding　experiments.