This　paper　is　concerned　with　optimal　model　averaging　procedure　for　semiparametric　partially　linear　models　where　some　covariates　are　subject　to　measurement　error.　We　proposed　a　corrected　semiparametric　generalized　least　squares　estimation　for　unknown　parameters　and　nonparametric　function,　and　developed　a　Mallows-type　criterion　for　weight　choice.　The　resulting　model　average　estimator　is　shown　to　be　asymptotically　optimal　in　terms　of　achieving　the　smallest　possible　squared　error　under　some　regularity　conditions.　The　simulation　studies　demonstrate　that　the　proposed　procedure　is　superior　to　traditional　model　selection　and　model　averaging　methods.　Our　approach　is　further　applied　to　Ragweed　Pollen　Level　data.　©　2023　Elsevier　B.V.