In　this　paper,　a　hidden　node　pruning　algorithm　based　on　the　neural　complexity　is　proposed,　the　entropy　of　neural　network　can　be　calculated　by　the　standard　covariance　matrix　of　the　neural　network＇s　connection　matrix　in　the　training　stage,　and　the　neural　complexity　can　be　acquired.　In　ensuring　the　information　processing　capacity　of　neural　network　is　not　reduced,　select　and　delete　the　least　important　hidden　node,　and　the　simpler　neural　network　architecture　is　achieved.　It　is　not　necessary　to　train　the　cost　function　of　the　neural　network　to　a　local　minimal,　and　the　pre-processing　neural　network　weights　is　avoided　before　neural　network　architecture　adjustment.　The　simulation　results　of　the　non-linear　function　approximation　shows　that　the　performance　of　the　approximation　is　ensured　and　at　the　same　time　a　simple　architecture　of　neural　networks　can　be　achieved.　©　2010　IEEE.