Using　machine　learning　methods　to　analyze　microarray　data　of　gastric　cancer　and　discover　novel　marker　gene　can　provide　suggestion　for　further　study　of　the　molecular　mechanism,　gene　level　diagnosis　and　treatment,　of　gastric　cancer.　Most　existing　methods　use　machine　learning　methods　to　extract　marker　gene　using　only　one　data　set.　This　paper　proposed　a　hybrid　genetic　algorithm　(GA)/support　vector　machine　(SVM)　approach　to　analyze　multi　gastric　cancer　microarray　dataset　in　parallel　and　select　marker　genes.　Three　datasets　are　analyzed.　The　experiment　was　performed　4580　times.　The　top　20　genes　with　highest　occurrence　times　in　the　final　populations　of　the　GA　(the　occurrence　times　can　represent　the　significance　of　classification　in　a　sense)　are　selected　as　marker　genes.　Based　on　these　genes　the　classification　accuracies　are　above　90%　in　each　of　the　three　datasets.　Meanwhile,　biological　significance　analyses　show　that　this　method　can　identify　the　tumor　related　genes　efficaciously.　These　genes　are　vital　for　human　gastric　cancer　diagnosis　and　classification.