BP　neural　network,　as　a　traditional　supervised　artificial　neural　network,　has　been　widely　used　in　the　field　of　prediction　and　classification　for　dealing　with　nonlinear　data　problems,　but　it　is　easy　to　get　local　minima　and　overfitting,　and　poor　generalization.　To　address　the　above　problems,　this　paper　uses　the　powerful　macroscopic　search　ability　of　genetic　algorithm(GA)　to　optimize　the　structure　and　training　process　of　BP　neural　network,　finds　the　globally　optimal　weights　and　thresholds,　and　proposes　a　nonlinear　data　prediction　model　based　on　GA　optimized　BP　neural　network.　The　experimental　results　display　that　the　prediction　results　obtained　by　the　BP　neural　network　optimized　with　GA　have　higher　accuracy　compared　with　the　single　BP　prediction　model,　and　the　prediction　accuracy　is　12%　higher　on　average,　and　the　overfitting　problem　is　also　solved,　and　the　mean　square　error(MSE)　is　reduced　to　the　order　of　10-4,　which　improves　the　generalization　of　the　model.　©　2023　IEEE.