For　the　design　of　the　neural　network　architecture,　a　pruning　algorithm　based　on　the　neural　complexity　is　proposed.　The　essence　is　to　calculate　the　entropy　of　neural　network　by　the　standard　covariance　matrix　of　the　neural　network＇s　connection　matrix　in　the　process　of　training,　and　the　network＇s　complexity　can　be　acquired.　In　the　premise　of　ensuring　the　information　processing　capacity　of　neural　network,　the　least　important　hidden　node　is　deleted.　It　is　not　necessary　to　train　the　cost　function　of　the　neural　network　to　a　local　minimal,　suitable　for　pruning　neural　network　architecture　on-line,　and　the　pre-processing　neural　network　weights　are　avoided　before　architecture　adjustment　of　the　neural　network.　The　simulation　results　of　the　typical　function　approximation　show　that　the　precision　of　the　approximation　is　ensured　and　at　the　same　time　a　simple　architecture　of　neural　networks　can　be　achieved.