To　solve　the　problem　of　complex　structure　for　the　discrete　Hopfield　neural　network　(DHNN),　a　structural　optimization　algorithm　based　on　the　contribution　rate　is　proposed.　The　singular　value　decomposition　method　is　used　to　design　the　connection　weights.　On　the　basis　of　the　design,　the　contribution　rate　method　is　adopted　to　prune　the　connection　weights.　The　structural　complexity　of　the　DHNN　is　reduced　after　structure　optimization,　and　it　makes　the　DHNN　with　sparse　network　structure　which　is　similar　to　biological　neural　network　realize　the　structure　optimization.　Finally,　the　water　quality　evaluation　and　digital　recognition　are　used　to　verify　the　effectiveness　and　feasibility　of　the　structural　optimization　algorithm,　and　also　demonstrate　the　effectiveness　and　applicability　of　the　proposed　algorithm　for　large　scale　DHNN.　©,　2015,　Northeast　University.　All　right　reserved.