With　the　increasing　popularity　of　online　social　networks,　more　and　more　people　are　posting　information,　updating　their　statuses,　and　searching　for　topics　there.　Massive　cross-media　big　data　has　been　gathered　by　online　social　networks,　with　high　dynamics,　context-sparsity,　and　cross-media　semantic　gaps.　In　addition,　it　can　be　challenging　to　understand　users＇　search　intentions　in　the　setting　of　social　networks.　The　above　problems　have　brought　severe　challenges　and　obstacles　to　cross-media　searches,　which　also　attracted　more　and　more　attention　on　social　networks.　As　a　relatively　new　research　topic　and　interest,　the　concept,　methodology,　and　overall　research　idea　of　cross-media　search　based　on　user　search　intention　understanding　are　not　evident　in　the　litera-ture.　The　research　also　lacks　a　unified　paradigm　and　relatively　complete　research　ideas　on　social　networks.　To　solve　these　problems,　we　reviewed　more　than　100　references　based　on　our　preliminary　exploration　and　research　experience　in　this　field　from　the　whole　process　involved.　We　also　detailed　methodology,　datasets,　evaluation　indicators,　experiment　evaluation,　and　research　trends　and　analyzed　the　challenges.　These　works　will　help　be-ginners　quickly　establish　research　ideas　and　processes　in　this　field,　and　enable　them　to　focus　on　algorithm　design　without　paying　too　much　attention　to　datasets,　evaluation　metrics,　and　research　frameworks.　We　believe　this　review　will　attract　more　researchers　to　focus　on　social　network　cross-media　search　based　on　user　search　intention　understanding　and　benefit　their　work.