This　paper　considers　a　specification　test　method　for　the　partially　linear　varying　coefficient　spatial　autoregressive　models　with　high-dimensional　covariates.　By　combining　the　one-dimensional　linear　projection　of　the　covariates　with　the　residual　marked　empirical　process,　we　propose　a　projection-based　test　method.　This　method　is　not　only　suitable　for　high-dimensional　covariates,　but　also　effectively　avoids　the　subjective　selection　of　parameters　(such　as　bandwidth).　Under　mild　conditions,　the　consistency　of　the　proposed　method　is　established.　Furthermore,　we　show　that　the　proposed　test　method　can　distinguish　Pitman　local　alternatives　converging　to　the　null　at　the　usual　parametric　rate.　To　improve　the　finite　sample　properties　of　the　proposed　test,　we　develop　a　wild　bootstrap　method　to　obtain　the　critical　values　or　p-values,　and　show　the　validity　of　the　proposed　bootstrap　method.　We　conduct　Monte　Carlo　studies　to　investigate　the　finite　sample　performance　of　our　new　procedure,　and　find　that　the　proposed　test　methods　produce　satisfactory　results.　Finally,　the　proposed　test　methods　are　illustrated　by　two　real　data　examples.　©　Korean　Statistical　Society　2025.