In　response　to　the　problem　of　low　recognition　rate　caused　by　low　quality　face　image　in　face　recognition,　a　face　quality　evaluation　method　based　on　deep　convolutional　neural　network　is　proposed.　Firstly,　the　depthwise　separable　convolution　method　is　adopted　to　establish　a　depth-convolution　model　which　contain　8　blocks　to　achieve　face　feature　extraction.　Secondly,　the　last　layer　of　the　convolution　module　connects　8　output　branches,　and　uses　regression　and　classification　methods　to　predict　the　probability　of　8　attributes,　namely　yaw,　pitch,　norm,　opening　mouth,　occlusion,　blur,　dim,　and　closed　eyes.　The　proposed　method　also　proves　that　norm　value　can　help　analyze　the　quality　of　face　image.　Finally,　the　weight　optimization　is　realized　by　taking　the　video　pass　rate　as　the　optimal　objective　function.　the　quality　score　of　face　image　can　be　calculated　by　weighting　each　branch　The　higher　the　score,　the　better　the　quality　of　the　face　image.　In　this　paper,　an　end-to-end　face　quality　evaluation　model　is　implemented　by　using　depthwise　separable　convolution　method.　The　parameters　of　the　model　are　less　than　60000,　the　operation　speed　is　fast,　and　the　evaluation　results　are　accurate;　and　it　can　filter　out　low-quality　face　image　in　real　time,　and　recommend　high-quality　face　image　to　face　recognition　model.