The　rapid　development　of　the　social　network　has　brought　great　convenience　to　people＇s　lives.　A　large　amount　of　cross-media　big　data,　such　as　text,　image,　and　video　data,　has　been　accumulated.　A　cross-media　search　can　facilitate　a　quick　query　of　information　so　that　users　can　obtain　helpful　content　for　social　networks.　However,　cross-media　data　suffer　from　semantic　gaps　and　sparsity　in　social　networks,　which　bring　challenges　to　cross-media　searches.　To　alleviate　the　semantic　gaps　and　sparsity,　we　propose　a　cross-media　search　method　based　on　complementary　attention　and　generative　adversarial　networks　(CAGS).　To　obtain　high-quality　feature　representations,　we　build　a　complementary　attention　mechanism　containing　the　focused　and　unfocused　features　of　images　to　realize　the　consistent　association　of　cross-media　data　in　social　networks.　By　designing　the　cross-media　adversarial　learning　process,　we　can　obtain　a　common　semantic　representation　of　cross-media　data　and　further　alleviate　the　semantic　gap　and　sparsity　issues　for　social　networks.　Finally,　we　perform　a　similarity　calculation　to　realize　an　accurate　cross-media　search.　We　construct　four　search　tasks　utilizing　two　standard　cross-media　data　sets　to　verify　the　search　performance　of　the　proposed　CAGS.