Since　it　is　difficult　for　deep　convolutional　neural　network　to　be　deployed　to　terminal　equipment　with　limited　resources,　this　paper　proposes　an　efficient,　compact,　and　lightweight　network　Mobile_BLNet,　which　achieves　a　good　balance　between　model　size,　computation,　and　performance.　The　network　uses　depthwise　separable　convolution　and　inverse　residual　structure,　reduces　the　scale　of　the　model　and　saves　a　lot　of　computing　resources　by　reasonably　allocating　the　amount　of　computation　of　different　branches.　The　total　ratio　method　is　used　to　prune　the　convolution　channel　with　low　contribution,　which　has　excellent　performance　under　the　same　compression　effect.　Model　reconstruction　is　based　on　the　clipping,　which　further　reduces　the　computational　resources.　The　experimental　results　show　that　Mobile_BLNet　has　excellent　performance.　On　CIFAR-10/CIFAR-100　dataset,　91.2%/71.5%　accuracy　is　obtained　with　0.1　M/0.3　M　parameters　and　9.6　M/12.7　M　floating　point　operations.　On　Food101/ImageNet　dataset,　82.8%/70.9%　accuracy　is　obtained　with　1.0　M/2.1　M　parameters　and　203.0　M/249.6　M　floating　point　operations.　The　network　meets　the　requirements　of　energy-efficient　and　lightweight　hardware　deployment.　©　2023　Chinese　Institute　of　Electronics.　All　rights　reserved.