Instead　of　picking　up　a　single　ridge　parameter　in　ridge　regression,　this　paper　considers　a　frequentist　model　averaging　approach　to　appropriately　combine　the　set　of　ridge　estimators　with　different　ridge　parameters,　when　the　response　is　randomly　right　censored.　Within　this　context,　we　propose　a　weighted　least　squares　ridge　estimation　for　unknown　regression　parameter.　A　new　Mallows-type　weight　choice　criterion　is　then　developed　to　allocate　model　weights,　where　the　unknown　distribution　function　of　the　censoring　random　variable　is　replaced　by　the　Kaplan-Meier　estimator　and　the　covariance　matrix　of　random　errors　is　substituted　by　its　averaging　estimator.　Under　some　mild　conditions,　we　show　that　when　the　fitting　model　is　misspecified,　the　resulting　model　averaging　estimator　achieves　optimality　in　terms　of　minimizing　the　loss　function.　Whereas,　when　the　fitting　model　is　correctly　specified,　the　model　averaging　estimator　of　the　regression　parameter　is　root-n　consistent.　Additionally,　for　the　weight　vector　which　is　obtained　by　minimizing　the　new　criterion,　we　establish　its　rate　of　convergence　to　the　infeasible　optimal　weight　vector.　Simulation　results　show　that　our　method　is　better　than　some　existing　methods.　A　real　dataset　is　analyzed　for　illustration　as　well.