We　consider　statistical　inference　for　additive　partial　linear　models　when　the　linear　covariate　is　measured　with　error.　A　bias-corrected　spline-backfitted　kernel　smoothing　method　is　proposed.　Under　mild　assumptions,　the　proposed　component　function　and　parameter　estimator　are　oracally　efficient　and　fast　to　compute.　The　nonparametric　function　estimator＇s　pointwise　distribution　is　asymptotically　equivalent　to　an　function　estimator　in　partial　linear　model.　Finite-sample　performance　of　the　proposed　estimators　is　assessed　by　simulation　experiments.　The　proposed　methods　are　applied　to　Boston　house　data　set.