With　the　rapid　development　of　artificial　intelligence,　especially　deep　learning　technology,　various　new　technologies　and　applications　based　on　face　images　have　emerged.　Obstructive　sleep　apnea　(OSA)　is　the　disease　with　the　highest　morbidity　and　the　most　serious　long-term　harm　among　childhood　sleep　breathing　disorders,　and　it　is　increasingly　receiving　common　attention　from　families　and　society.　Children　with　the　disease　have　a　special　facial　appearance　and　require　early　identification　and　treatment　to　prevent　it.　However,　the　current　diagnostic　methods　have　problems　such　as　being　invasive,　time-consuming,　and　expensive.　The　purpose　of　this　article　is　to　use　graph　neural　network　technology　based　on　face　images　to　establish　an　OSA　auxiliary　diagnosis　strategy　for　children　to　achieve　OSA　screening　and　analysis.　Therefore,　this　article　first　takes　the　facial　landmarks　as　the　analysis　object,　divides　the　face　into　six　key　areas,　and　selects　important　landmarks　in　these　regions.　On　this　basis,　to　better　consider　the　relationship　between　important　landmarks,　a　global　collaborative　recognition　strategy　is　proposed.　By　extracting　the　implicit　relationship　between　landmarks,　face　graph　structure　data　is　established.　Finally,　the　OSA-GNN　model　is　established　to　achieve　OSA　screening　and　auxiliary　analysis　in　children.　Compared　with　other　related　studies,　this　strategy　not　only　has　a　stronger　representation　and　generalisation　ability　but　can　also　carry　out　clinical　applications　better,　providing　doctors　with　diagnostic　suggestions.