In　this　paper,　a　class　of　partially　linear　additive　spatial　autoregressive　models　(PLASARM)　is　studied.　With　the　nonparametric　functions　approximated　by　basis　functions,　we　propose　a　generalized　method　of　moments　estimator　for　PLASARM.　Under　mild　conditions,　we　obtain　the　asymptotic　normality　for　the　finite　parametric　vector　and　the　optimal　convergence　rate　for　nonparametric　functions.　In　order　to　make　statistical　inference　for　parametric　component,　we　propose　the　estimator　for　asymptotic　covariance　matrix　of　the　parameter　estimator　and　establish　the　asymptotic　properties　for　the　resulting　estimators.　Finite　sample　performance　of　the　proposed　method　is　assessed　by　Monte　Carlo　simulation　studies,　and　the　developed　methodology　is　illustrated　by　an　analysis　of　the　Boston　housing　price　data.　(C)　2018　Elsevier　B.V.　All　rights　reserved.