Aiming　at　the　problem　that　targets　are　subject　to　occlusion,　deformation,　and　complex　background　interference　in　the　drone　video,　a　Unmanned　Aerial　Vehicle　(UAV)　target　tracking　algorithm　based　on　the　adaptive　depth　network　is　proposed.　First,　based　on　the　Principal　Component　Analysis　(PCA)　and　Convolutional　Neural　Network　(CNN),　a　3-order　adaptive　CNN　network　is　designed　for　target　feature　extraction.　PCA　is　hierarchically　performed　on　H,S,　and　I　channels,　convolving　hierarchically　by　the　obtained　eigenvectors,　which　optimizes　the　network　structure　and　improves　the　convergence　speed　and　accuracy.　Second,　the　target　depth　feature　is　input　into　KCF　algorithm　for　target　tracking.　By　analyzing　the　change　rate　of　the　two　adjacent　frames　and　using　the　segmented　adaptive　adjustment　of　learning　rate　to　update　the　target　template,　the　target　occlusion　problem　is　effectively　moderated.　The　experimental　results　show　that　the　algorithm　effectively　avoids　the　degradation　of　tracking　accuracy　caused　by　complex　factors,　reaching　good　robustness.　The　average　accuracy-rate　of　the　algorithm　is　9.62%　higher　than　that　of　fully　convolutional　network　based　tracker　Fully　Convolutional　Network　Tracking　(FCNT),　and　the　average　success-rate　is　increased　by　11.9%.　©　2019,　Press　of　Chinese　Journal　of　Aeronautics.　All　right　reserved.