Echo　State　Network　(ESN)　is　a　recurrent　neural　network　with　a　large,　randomly　generated　recurrent　part　called　the　dynamic　reservoir.　Only　the　output　weights　are　modified　during　training.　However,　proper　balancing　of　the　trade-off　between　the　structure　and　performance　for　ESN　remains　a　difficult　task.　In　this　paper,　a　structure　optimized　method　for　ESN　based　on　contribution　is　proposed　to　simplify　its　network　structure　and　improve　its　performance.　First,　we　evaluate　the　contribution　of　reservoir　neurons.　Second,　we　present　a　pruning　mechanism　to　remove　the　unimportant　connection　weights　of　reservoir　neurons　with　low　contribution.　Finally,　the　new　output　weights　are　learned　with　the　pseudo　inverse　method.　The　novel　optimized　ESN,　named　C-ESN,　is　tested　on　a　Lorenz　chaotic　time-series　prediction　and　an　actual　municipal　sewage　treatment　system.　The　simulation　results　show　that　the　C-ESN　can　have　better　prediction　and　generalization　performance　than　ESN.