The tracking algorithm always receives an inaccurate object position because of the poor robustness of the features in the multi-domain network tracking (MDNet) algorithm network model and the loss of the target background information. In this study, we propose a multi-domain convolutional neural network visual tracking algorithm based on the combined reconstructed features. This algorithm performs the deconvolution upsampling operation on an advanced object feature to obtain reconstructed features containing the background information. This advanced object feature is extracted using the end convolutional layer and is combined with the reconstructed feature, which can enhance the robustness of the feature and effectively distinguish an object from the background,thereby improving the object tracking accuracy in situations such as object occlusion, illumination change, and object deformation. The proposed algorithm is tested using the OTB50 and VOT2015 tracking test sets. When compared with the MDNet algorithm, the tracking accuracy and tracking success rate of the proposed algorithm are improved by 1.53% and 2.03%, respectively.