UAVs　have　been　widely　used　in　military　and　civilian　applications.　Target　tracking　is　one　of　the　key　technologies　for　UAV　applications.　Aiming　at　the　problem　that　the　target　is　prone　to　deformation　and　occlusion　during　the　tracking　process　of　the　UAV,　a　target　tracking　algorithm　for　UAV　based　on　adaptive　Siamese　network　is　proposed.　Firstly,　using　two　convolution　networks,　a　5-layer　Siamese　network　is　constructed.　The　target　location　is　obtained　by　convolving　the　template　features　with　the　current　frame　image　features.　Secondly,　the　Gaussian　mixture　model　is　used　to　model　the　previous　prediction　results　and　establish　the　target　template　library.　Thirdly,　the　most　reliable　target　template　is　selected　from　the　template　library　to　update　the　matching　template　of　the　Siamese　network,　so　that　the　Siamese　network　can　adapt　to　the　target.　Finally,　a　regression　model　is　introduced　to　further　pinpoint　the　target　location　and　reduce　the　impact　of　background　on　network　performance.　The　results　show　that　the　algorithm　effectively　reduces　the　influence　of　deformation　and　occlusion　on　tracking　performance　and　are　highly　accurate.　©　2020,　Press　of　Chinese　Journal　of　Aeronautics.　All　right　reserved.