Visual tracking remains a challenging problem due to the scale change,deformatio n,occlusion,motion blur and environmental change of the target.A kernelized correlation filter object-tracking approach combined with atte ntion mechanism is proposed in this paper,utilizing attention model and convolution features to improve the robustness of the kernel ized correlation filter algorithm.The convolution neural network is introduced to extract the convolution features.T he attention weights are obtained using the similarity matrix of two samples,and combined with kerneliz ed correlation filters.Two classifiers are used to detect objects and background,and the adaptive update of the model is ach ieved according to the response values of the two classifiers.Compared with the original kernelized correlation filter algorit hm on typical video sequences in public data sets, the success rate and precision have been promoted by 18.9% and 58.7% in this pap er.The experimental results on public data sets show that the proposed approach maintains its good robustness and adaptability even in complex scenes.