To　optimize　the　reservoir　design　of　echo　state　networks　(ESN),　the　Chung-Lu　(CL)　construction　algorithm　based　on　random　matrix　theory　was　applied　to　generate　flexible　and　efficient　scale-free　networks.　In　response　to　the　degree　deviation　that　occurs　during　the　construction　process,　random　pruning　or　degree　pruning　was　used　to　improve　the　scale-free　network.　The　robustness　of　the　reservoir　was　improved　by　simulating　random　or　targeted　attacks.　Experimental　results　show　that　the　CL　algorithm　with　a　pruning　mechanism　not　only　constructs　a　scale-free　network　with　power-law　property,　but　also　has　significantly　better　construction　speed　and　prediction　performance　than　the　baseline　algorithms,　and　the　effect　of　random　pruning　was　better　than　that　of　degree　pruning.　Compared　with　the　optimal　result　of　the　baseline　algorithm,　the　running　time　and　prediction　error　of　ESN　constructed　by　the　CL　algorithm　based　on　random　pruning　were　at　least　reduced　by　14.2%　and　10.6%.　©　2025　Zhejiang　University.　All　rights　reserved.