Prediction　of　Apache　Spark　job　execution　time　is　a　key　technology　to　guide　Spark　cluster　resource　allocation　and　parameter　tuning.　In　the　existing　research,　a　unified　modeling　method　is　used　for　different　jobs,　and　the　prediction　model　considers　less　factors,　resulting　in　poor　prediction　effect.　In　view　of　the　above　problems,　this　paper　proposes　a　classification-based　Spark　job　performance　modeling　method.　The　method　first　selects　features　that　are　strongly　correlated　with　job　execution　time,　then　classifies　jobs　according　to　the　selected　features,　and　finally　uses　GBDT　algorithm　to　build　an　execution　time　prediction　model　for　each　class　of　jobs　classified.　The　experimental　results　show　that,　compared　with　the　method　using　unified　modeling,　the　method　proposed　in　this　paper　can　reduce　the　RMSE　and　MAPE　of　the　prediction　results　by　an　average　of　42.5%　and　51.1%.　©　2022　SPIE