Aiming　to　solve　the　problem　of　selecting　echo　state　network　parameters,　a　method　for　optimizing　echo　state　network　(ESN)　parameters　based　on　the　behavior　space　is　proposed.　The　essence　is　to　construct　ESN　behavior　space　through　generalization　rank,　kernel　rank,　and　memory　capacity.　The　optimization　algorithm　a-dopted　the　novel　search　genetic　algorithm　(NSGA),　which　combines　the　K-nearest　neighbor　individual　distance　and　normalized　mean　squared　error,　limits　the　genetic　direction　of　the　genetic　algorithm　by　establishing　the　minimum　configuration　of　the　behavior　space　to　screen　genes,　therefore　improves　the　optimization　efficiency.　Next,　the　factors　that　affect　network　performance　were　determined.　This　method　overcomes　traditional　ESN　parameter　selection　difficulties,　the　long　optimization　time　of　the　genetic　algorithm,　and　no　suitable　theory　to　clarify　the　mpact　of　reservoir　performance　on　tasks.　As　a　result,　it　improved　the　optimization　efficiency　and　network　earning　performance.　The　experimental　results　showed　that　the　proposed　NSGA-ESN　method　optimizes　ESN　parameters　close　to　the　best　network　structure,　with　a　better　learning　performance　compared　to　the　growing　echo　state　network.　Furthermore,　the　factors　that　affect　the　performance　of　the　ESN　network　can　be　explained　through　the　behavior　space.　©　2021　The　Authors.　All　rights　reserved.