Image　topic　representation　in　social　networks　is　vital　for　people　to　get　significant　and　valuable　content.　However,　this　task　is　difficult　and　challenging　due　to　the　complexity　of　image　features.　This　paper　proposes　a　multifeature　complementary　attention　mechanism　for　image　topic　representation　named　CATR.　CATR　uses　scene-level　and　instance-level　object　detection　methods　to　obtain　the　object　information　on　social　networks.　Here,　the　image　features　are　divided　into　focused　features　and　unfocused　features.　Focused　features　are　used　to　learn　and　express　semantic　information,　while　unfocused　features　are　used　to　filter　out　noise　information　in　focused　feature　extraction.　The　attention　mechanism　is　constructed　by　combining　the　object　features　and　the　features　of　the　image　itself,　while　the　image　topic　representation　in　social　networks　is　realized　by　the　complementary　attention　mechanism.　Based　on　the　real　image　data　of　Sina　Weibo　and　Mir-Flickr　25K,　several　groups　of　comparative　experiments　are　constructed　to　verify　the　performance　of　the　proposed　CATR　by　leveraging　different　evaluation　measures.　The　experimental　results　demonstrate　that　the　proposed　CATR　obtains　an　optimal　accuracy　and　significantly　outperforms　the　other　comparison　methods　in　image　topic　representation.