Deep　learning　has　achieved　impressive　results　in　many　areas,　but　the　deployment　of　edge　intelligent　devices　is　still　very　slow.　To　solve　this　problem,　we　propose　a　novel　compression　and　acceleration　method　based　on　data　distribution　characteristics　for　deep　neural　networks,　namely　Pruning　Filter　via　Gaussian　Distribution　Feature　(PFGDF).　Compared　with　previous　advanced　pruning　methods,　PFGDF　compresses　the　model　by　filters　with　insignificance　in　distribution,　regardless　of　the　contribution　and　sensitivity　information　of　the　convolution　filter.　PFGDF　is　significantly　different　from　weight　sparsification　pruning　because　it　does　not　require　the　special　accelerated　library　to　process　the　sparse　weight　matrix　and　introduces　no　more　extra　parameters.　The　pruning　process　of　PFGDF　is　automated.　Furthermore,　the　model　compressed　by　PFGDF　can　restore　the　same　performance　as　the　uncompressed　model.　We　evaluate　PFGDF　through　extensive　experiments,　on　CIFAR-10,　PFGDF　compresses　the　convolution　filter　on　VGG-16　by　66:62%　with　more　than　90%　parameter　reduced,　while　the　inference　time　is　accelerated　by　83:73%　on　Huawei　MATE　10.