The　development　of　subways　is　currently　at　its　peak　and　underground　structures　including　subway　stations　are　being　constructed　on　a　large　scale.　However,　previous　research　on　seismic　risk　evaluation　of　underground　structures　mainly　focuses　on　the　analysis　of　influencing　factors　and　seismic　fragility.　There　is　a　lack　of　research　on　comprehensive　automated　seismic　risk　evaluation　from　a　macro　perspective　based　on　economic　losses,　casualties,　disaster　relief　costs　and　post-earthquake　recovery　time　etc.　The　emergence　of　semantic　web　technologies　provides　an　effective　way　for　multi-objective　automated　seismic　risk　evaluation　of　subway　stations.　This　paper　aims　to　develop　a　framework　for　comprehensive　seismic　risk　evaluation　of　subway　stations　based　on　Monte　Carlo　simulation　and　ontology　theory.　In　the　developed　framework,　the　seismic　risk　probabilities　of　subway　stations　are　determined　using　Monte　Carlo　simulation.　Taking　economic　losses,　casualties,　disaster　relief　costs　and　post-earthquake　recovery　time　as　indicators,　an　ontology-based　model　for　seismic　risk　evaluation　of　subway　stations　is　then　developed　by　combining　the　knowledge　base　and　the　semantic　web　rules　to　achieve　automated　evaluations.　A　case　study　is　also　conducted　to　demonstrate　the　practicability　of　the　proposed　framework　and　further　validate　the　semantic　web　rule　language　and　the　semantic　query-enhanced　web　rule　language　©　2023　Elsevier　Ltd