To　overcome　the　problems　of　small　target　occupation　and　vulnerability　to　interference　of　complex　background　information　in　the　UAV　video　tracking　process,　an　adaptive　fusion　network-based　UAV　target　tracking　algorithm　is　proposed.　First,　a　deep　network　model　is　constructed　based　on　the　receptive　field　block　and　the　residual　network,　which　can　effectively　extract　target　features　and　enhance　the　effective　receptive　field　of　the　features.　Second,　a　multi-scale　adaptive　fusion　network　is　proposed,　which　can　adaptively　fuse　the　semantic　features　of　the　deep　network　and　detailed　features　of　the　shallow　network　to　enhance　the　expression　capability　of　the　features.　Finally,　the　fused　target　features　are　input　into　the　correlation　filtering　model,　and　the　maximum　confidence　score　of　the　response　map　is　calculated　to　determine　the　tracking　target　location.　The　simulation　experimental　results　show　that　the　algorithm　achieves　a　high　rate　of　tracking　success　and　accuracy,　and　can　effectively　improve　the　performance　of　UAV　target　tracking　algorithm.　©　2022　AAAS　Press　of　Chinese　Society　of　Aeronautics　and　Astronautics.　All　rights　reserved.