To　reduce　the　high　pre-weaning　mortality　rate　of　new-born　piglets　crushed　by　sows,　a　kind　of　recognition　and　evaluation　method　for　sows＇　behavior　based　on　the　scheme　of　time-sharing　and　multiplexing　by　adopting　triaxial　acceleration　and　video　sensors　at　day　and　night　is　proposed　in　this　paper.　For　darker　scene　at　night,　random　forest　classifier　with　optimal　43-dimensional　feature　vector　subset　proposed　in　this　paper　is　adopted　to　recognize　four　kinds　of　macro　behaviors　of　sows　roughly　by　adopting　triaxial　acceleration　sensor　MPU6050.　The　recognition　rate　can　reach　89.4%.　For　brighter　light　scene　during　the　day,　an　improved　bilinear　convolutional　neural　network　method　based　on　CBAM　module　is　proposed　in　this　paper　to　recognize　seven　kinds　of　micro　behaviors　of　sows　by　video　sensor.　The　recognition　rate　can　reach　84.4%.　The　methods　proposed　in　this　paper　can　meet　the　requirement　of　real-time　to　recognize　the　behavior　of　sows　during　24　hours　on　the　premise　of　ensuring　accuracy.　Finally,　an　evaluation　model　of　sows＇　maternal　behavior　level　is　set　up　in　this　paper.　The　achivement　of　the　study　can　not　only　help　the　farm　to　select　sows　with　higher　maternal　ability　for　breeding　piglets,　but　also　avoid　the　large-scale　economic　losses　caused　by　the　high　mortality　rate　of　piglets　before　weaning　of　the　farm.