Visual　trackers　have　achieved　a　high-level　performance　from　deep　features,　but　many　limitations　remain.　Online　trackers　suffer　from　low　speed　while　using　deep　features　for　parameter　updating,　and　deep　trackers　trained　offline　demonstrate　data　hunger.　To　meet　these　challenges,　our　work　aims　to　mine　the　target　representation　capability　of　a　pre-trained　model　and　presents　deep　convolutional　descriptor　aggregation　(DCDA)　for　visual　tracking.　Based　on　spatial　and　semantic　priors,　we　propose　an　edge-aware　selection　(EAS)　and　a　central-aware　selection　(CAS)　method　to　aggregate　the　accuracy-aware　and　robustness-aware　features.　To　make　full　use　of　the　scene　context,　our　method　is　derived　from　one-shot　learning　by　designing　a　dedicated　regression　process　that　is　capable　of　predicting　discriminative　model　in　a　few　iterations.　By　exploiting　robustness　feature　aggregation,　the　accuracy　feature　aggregation,　and　the　discriminative　regression,　our　DCDA　with　Siamese　tracking　architecture　not　only　enhances　the　target　prediction　capacity,　but　also　achieves　a　low-cost　reuse　of　the　pre-trained　model.　Comprehensive　experiments　on　OTB-100,　VOT2016,　VOT2017,　VOT2020,　NFS30,　and　NFS240　show　that　our　DCDA　tracker　achieves　state-of-the-art　performance　with　a　high　running　speed　of　65　FPS.　The　source　code　and　all　the　experimental　results　of　this　work　will　be　made　public　at　https://github.com/Gitlyz007/DCDA_Tracker.