Convolutional　neural　network　(CNN)　is　recognized　as　state　of　the　art　of　deep　learning　algorithm,　which　has　a　good　ability　on　the　image　classification　and　recognition.　The　problems　of　CNN　are　as　follows:　the　precision,　accuracy　and　efficiency　of　CNN　are　expected　to　be　improved　to　satisfy　the　requirements　of　high　performance.　The　main　work　is　as　follows:　Firstly,　wavelet　convolutional　neural　network　(wCNN)　is　proposed,　where　wavelet　transform　function　is　added　to　the　convolutional　layers　of　CNN.　Secondly,　wavelet　convolutional　wavelet　neural　network　(wCwNN)　is　proposed,　where　fully　connected　neural　network　(FCNN)　of　wCNN　and　CNN　are　replaced　by　wavelet　neural　network　(wNN).　Thirdly,　image　classification　experiments　using　CNN,　wCNN　and　wCwNN　algorithms,　and　comparison　analysis　are　implemented　with　MNIST　dataset.　The　effect　of　the　improved　methods　are　as　follows:　(1)　Both　precision　and　accuracy　are　improved.　(2)　The　mean　square　error　and　the　rate　of　error　are　reduced.　(3)　The　complexitie　of　the　improved　algorithms　is　increased.