An　excellent　unsupervised　tracker　includes　a　powerful　base　tracker　and　an　effective　unsupervised　tracking　strategy.　However,　most　base　trackers　lack　internal　feature　representations　for　information　embedding　processes.　Most　unsupervised　trackers　are　not　robust　enough　in　complex　environments　and　lack　an　effective　template　update　strategy.　We　propose　an　unsupervised　object　tracking　based　on　graph　attention　information　embedding　(UAT)　to　solve　the　above　problems.　UAT　combines　graph　attention　mechanism　with　multi-scale　features　to　construct　a　multi-scale　graph　attention　module　(MGA).　MGA　module　dynamically　and　efficiently　completes　the　information　embedding　between　the　template　branch　and　the　search　area　branch.　The　response　map　obtained　by　fusing　the　feature　maps　of　the　two　branches　is　more　informative　about　the　location　of　the　target.　An　attention　based　information　reinforcement　update　module　(RUM)　improves　the　robustness　of　the　tracker.　RUM　enhances　the　representation　of　the　feature　map　in　both　the　spatial　dimension　and　the　channel　dimension.　Template　features　are　also　updated　indirectly　through　information　transfer　between　the　two　branches.　RUM　suppresses　background　interference　and　improves　network　perception　during　tracking.　Experiments　on　challenging　benchmarks　such　as　VOT2018,　VOT2019,　TrackingNet,　OTB100,　LaSOT　and　UAV123　demonstrate　that　the　proposed　UAT　achieves　state-of-the-art　performance　in　unsupervised　trackers.