Single object tracking is a crucial task in computer vision, aiming to accurately locate a target in a video sequence. Although deep learning has significantly advanced the field of single object tracking, challenges such as target deformation, complex backgrounds, occlusion, and scale variation still remain. This paper systematically reviews the development of deep learning-based single object tracking methods over the past decade, covering traditional sequence models based on convolutional neural networks, recurrent neural networks, and Siamese networks, as well as hybrid architectures combining convolutional neural networks with Transformers and the latest approaches entirely based on Transformers. Furthermore, we evaluate the performance of different methods in terms of accuracy, robustness, and computational efficiency on benchmark datasets such as OTB100, LaSOT, and GOT-10k, followed by an in-depth analysis. Finally, we discuss the future research directions of deep learning-based single object tracking algorithms.