With　the　development　of　science　and　technology　and　the　continuous　improvement　of　people＇s　living　standards,　the　traditional　staff　quality　evaluation　can　no　longer　meet　the　needs　of　production　and　life,　and　the　BP　neural　network　has　also　appeared　many　shortcomings　in　practical　applications.　This　article　mainly　studies　the　company＇s　employee　quality　evaluation　model　based　on　BP　neural　network.　This　article　first　collects　and　preprocesses　employees＇　usual　performance　data,　and　then　predicts　their　corresponding　quality　scores　based　on　BP　neural　network.　And　use　MATLAB　software　to　simulate　the　constructed　prediction　model,　and　finally　develop　a　complete　set　of　employee　performance　data　prediction　system　based　on　this　model,　so　as　to　achieve　the　purpose　of　employee　quality　evaluation.　The　experimental　data　in　this　paper　shows　that　the　average　relative　error　of　model　training　output　tends　to　be　stable.　After　the　40th　iteration　of　training,　the　average　relative　error　of　model　training　can　reach　0.0128.　After　the　prediction　model　training　was　completed,　15　sets　of　verification　samples　were　used　to　verify　the　model.　The　verification　results　found　that　the　average　relative　error　of　the　model　converged,　so　the　model　did　not　overfit.　Experimental　results　show　that　although　BP　neural　network　has　two　excellent　functions　of　adaptive　and　nonlinear　approximation,　it　can　solve　the　complex　nonlinear　relationship　between　normal　performance　and　overall　performance.　But　BP　neural　network　still　has　its　own　inevitable　shortcomings　in　some　aspects.　For　example　the　redundancy　between　the　employee　scoring　sample　data;　the　problem　that　the　input　variable　dimensionality　is　too　high,　which　leads　to　the　low　efficiency　of　the　model;　the　fuzzy　neural　network　is　easy　to　fall　into　the　local　optimum　and　it　is　difficult　to　find　the　global　optimum.