The　high-risk　behaviour　of　pigs　from　standing,　sitting　to　lying　and　grovelling　is　the　main　reason　that　the　piglets　are　always　crushed　to　death.　This　paper　conducts　research　based　on　the　field　of　classification　and　recognition　of　pig　behaviours　by　using　triaxial　acceleration　sensor,　which　can　evaluate　the　maternal　ability　of　pigs　and　provide　data　basis　for　selecting　high　quality　breeding　pigs.　Aiming　at　the　problem　of　low　data　variability　and　small　data　range　due　to　the　small-scale　activity　of　the　pigs,　which　can　cause　poor　classification　accuracy.　This　paper　first　performs　moving　average　filter　processing　on　the　x,　y,　and　z-axis　data　collected　by　the　triaxial　acceleration　sensor.　After　feature　extraction　and　feature　selection,　an　optimal　feature　subset　is　proposed.　Experiments　show　that　by　adopting　the　optimal　feature　subset　proposed,　the　random　forest　classifier　adopted　can　classify　and　evaluate　four　basic　behaviours　of　pigs　in　daily　life　better.　The　accuracy　on　the　test　set　reaches　93.8%.　Compared　with　decision　tree　and　BP　neural　network,　the　AUC　value　of　random　forest　reaches　0.957,　which　has　obvious　performance　advantages.　Finally,　this　paper　also　proposed　an　evaluation　model　of　maternal　ability　for　pigs　and　adopted　maternal　ability　index　to　evaluate　the　maternal　ability　of　the　pigs　according　to　the　classification　results.　©　Published　under　licence　by　IOP　Publishing　Ltd.