To address the tracking stability degradation caused by target scale variation, deformation, illumination variation, and background clutter in complex scenes, a target tracking algorithm based on adaptive multilayer convolutional feature decision fusion is proposed. Initially, multilayer convolutional features are extracted from a target candidate region using the VGG-Net-19 convolutional neural network. Then, under a correlation filter model framework, the extracted convolutional features are employed to construct several weak trackers. Decision weights are adjusted adaptively based on the fluctuation of the decision losses of these weak trackers, and the target position is estimated based on the multilayer convolutional features. Next, according to a scale correlation filter model, multiple scale image patches are sampled at the target center position. Taking advantage of the prior distribution of scale variation between adjacent frames, its scale is predicted. Fifty-one video sequences with multiple challenging attributes are selected to evaluate the tracking performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm has higher tracking accuracy and success rate compared with state-of-the-art target tracking algorithms. The proposed algorithm adapts well to target scale variation. In addition, it improves the target tracking robustness under target deformation, illumination variation, and background clutter conditions.