According to the edge cloud collaborative deep learning target detection method based on target tracking acceleration provided by the invention, the problem that the real-time performance of the target detection problem cannot be guaranteed is solved. The method comprises the following three stages of processing: in the first stage, a key frame selection method is provided by using a self-adaptive key frame algorithm on an edge node, and key frames are selected in the same deep learning model only by extremely low computing resource cost; and a second stage, at the cloud, high-precision target detection is peformed by using the edge screened data and a high-precision classification model; and a third stage, at an edge end, rapid tracking is carried out through classification of key frames and a frame marking result by utilizing a twin network, and according to the method, data screening aiming at video target detection is realized by utilizing an adaptive key frame algorithm, and meanwhile, compromise between model precision and time delay consumption is realized. A reliable scheme is provided for solving the video target detection problem of the edge cloud collaborative deep learning model.