In high efficiency video coding HEVC, the latest λdomain rate control scheme adaptively allocates the number of bits for each pixel. In the process of adaptively allocating the number of bits, the encoding quality of the target object is not considered.Aiming at this problem, a video multi target rate allocation scheme based on deep learning is proposed. In this scheme, a target detection algorithm single shot multibox detector (SSD) is first used to detect and identify multi objects in a coded video to obtain object information. Then in the frame level code rate allocation process, according to the difference between frames, an adaptive bit rate is used to allocate a reasonable number of bits for each frame of video. Finally, the quantization quality of the target object is improved with a smaller quantization parameter (QP). The experimental results show that compared with HM16.9, the proposed algorithm improves the global average quality by 0.15 dB and the quality of the target area by 0.35 dB when the code control accuracy is almost constant.