We　study　model　selection　and　model　averaging　in　semiparametric　partially　linear　models　with　missing　responses.　An　imputation　method　is　used　to　estimate　the　linear　regression　coefficients　and　the　nonparametric　function.　We　show　that　the　corresponding　estimators　of　the　linear　regression　coefficients　are　asymptotically　normal.　Then　a　focused　information　criterion　and　frequentist　model　average　estimators　are　proposed　and　their　theoretical　properties　are　established.　Simulation　studies　are　performed　to　demonstrate　the　superiority　of　the　proposed　methods　over　the　existing　strategies　in　terms　of　mean　squared　error　and　coverage　probability.　Finally,　the　approach　is　applied　to　a　real　data　case.