The　semiparametric　regression　models　contain　both　parametric　and　nonparametric　components,and　retain　the　flexibility　of　nonparametric　models　while　avoiding　the　curse　of　dimensionality.　To　address　these　models,procedures　combined　the　common　methods　in　the　parametric　with　these　in　the　nonparametric　models　were　developed　in　recent　years.　Nevertheless,it　brings　challenges　for　our　work　since　the　complexity　and　difficulty　of　these　procedures　are　more　than　a　single　type　regression　model.　Unlike　statistical　inference　for　regression　coefficients　in　the　literature,this　paper　considers　the　problem　of　variance　estimation　in　partial　linear　variable　coefficient　semiparametric　models.　By　using　the　local　constant　function　coefficient,the　semiparametric　model　can　be　converted　into　a　high　dimensional　linear　model.　Then　the　variance　estimation　based　on　the　least　square　method　is　constructed,and　the　asymptotic　normality　for　the　resulting　estimator　is　also　established.　To　reduce　the　mean　square　error　of　the　least　squares　estimator,a　regularized　least　squares　method　named　ridge　estimator　is　proposed.　Finally,the　numerical　simulations　are　conducted　to　illustrate　the　finite　sample　performance　of　the　proposed　two　estimation　methods.　©2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.