The self-attention mechanism is used to enhance context in the visual object tracking algorithm, but in the face of complex scenes, the correlation in the selfattention mechanism is prone to mismatch. Therefore, a high-order target aware and similarity matching object tracking algorithm was proposed. A high-order target aware model was Constructed for the first-order self-attention map in the self-attention mechanism, the collapsed polarization filtering method was used to perform orthogonal modeling of space and channel dimensions, and optimize internal correlation. At the same time, a nonlinear fitting function was combined to avoid information loss caused by collapse, and then a high-order selfattention map is obtained to capture perceptual features with high-order context information. The perceptual features of the target were decomposed in different dimensions to refine the matching area, so the background noise was suppressed and the response map of the current frame was constrained, and improve the discriminative power of the network. The experimental results on OTB100 and UAV123 benchmarks show that the proposed algorithm has better tracking performance, and can effectively deal with problems such as similar interference.