In　the　process　of　complex　chemical　modeling,　the　traditional　static　neural　network　modeling　can　not　meet　certain　accuracy　because　of　the　time　sequence,　high　nonlinearity　and　high　dimension　of　process　data.　To　solve　this　problem,　a　feature　extracted　from　auto-encoder　based　echo　state　network　(FEAE-ESN)　is　proposed　in　this　paper.　In　the　traditional　echo　state　network　(ESN)　method,　the　number　of　nodes　in　the　reserve　pool　of　ESN　is　large,　and　then　the　dimension　of　the　reserve　pool　output　is　very　high.　Therefore,　to　solve　this　problem,　the　auto-encoder　is　used　to　extract　features　from　the　reserve　pool　output　of　well-trained　ESN.　Through　the　feature　extraction　of　auto-encoder,　on　one　hand,　the　dimension　of　the　output　of　the　reserve　pool　can　be　effectively　reduced,　thereby　reducing　the　complexity　of　the　data;　on　the　other　hand,　the　collinearity　of　the　outputs　of　the　original　reserve　pool　can　be　removed　through　extracting　features,　which　can　further　improve　the　calculation　performance　of　generalized　inverse.　Ultimately,　the　modeling　accuracy　of　ESN　is　improved.　The　proposed　FEAE-ESN　is　applied　to　modeling　the　Tennessee-Eastman　process.　The　simulation　results　verify　the　effectiveness　of　the　proposed　method.　©　All　Right　Reserved.