In　this　paper,　we　propose　a　model　averaging　estimation　for　the　varying-coefficient　partially　linear　models　with　missing　responses.　Within　this　context,　we　construct　a　HRCp　weight　choice　criterion　that　exhibits　asymptotic　optimality　under　certain　assumptions.　Our　model　averaging　procedure　can　simultaneously　address　the　uncertainty　on　which　covariates　to　include　and　the　uncertainty　on　whether　a　covariate　should　enter　the　linear　or　nonlinear　component　of　the　model.　The　simulation　results　in　comparison　with　some　related　strategies　strongly　favor　our　proposal.　A　real　dataset　is　analyzed　to　illustrate　the　practical　application　as　well.