The　natural　interaction　between　the　prosthetic　hand　and　the　upper　limb　amputation　patient　is　important　and　directly　affects　the　rehabilitation　effect　and　operation　ability.　Most　previous　studies　only　focused　on　the　interaction　of　gestures　but　ignored　the　force　levels.　This　paper　proposes　a　simultaneous　recognition　method　of　gestures　and　forces　for　interaction　with　a　prosthetic　hand.　The　multitask　classification　algorithm　based　on　a　convolutional　neural　network　(CNN)　is　designed　to　improve　recognition　efficiency　and　ensure　recognition　accuracy.　The　offline　experimental　results　show　that　the　algorithm　proposed　in　this　study　outperforms　other　methods　in　both　training　speed　and　accuracy.　To　prove　the　effectiveness　of　the　proposed　method,　a　myoelectric　prosthetic　hand　integrated　with　tactile　sensors　is　developed,　and　surface　electromyography　(sEMG)　datasets　of　healthy　persons　and　amputees　are　built.　The　online　experimental　results　show　that　the　amputee　can　control　the　prosthetic　hand　to　continuously　make　gestures　under　different　force　levels,　and　the　effect　of　hand　coordination　on　the　hand　perception　of　amputees　is　explored.　The　results　show　that　gesture　classification　operation　tasks　with　different　force　levels　based　on　sEMG　signals　can　be　accurately　recognized　and　comfortably　interact　with　prosthetic　hands　in　real　time.　It　improves　the　amputees＇　operation　ability　and　relieves　their　muscle　fatigue.