This　paper　is　concerned　with　model　averaging　procedure　for　varying-coefficient　partially　linear　models.　We　proposed　a　jackknife　model　averaging　method　that　involves　minimizing　a　leave-one-out　cross-validation　criterion,　and　developed　a　computational　shortcut　to　optimize　the　cross-validation　criterion　for　weight　choice.　The　resulting　model　average　estimator　is　shown　to　be　asymptotically　optimal　in　terms　of　achieving　the　smallest　possible　squared　error.　The　simulation　studies　have　provided　evidence　of　the　superiority　of　the　proposed　procedures.　Our　approach　is　further　applied　to　a　real　data.