As one of the indispensable basic branches of computer vision, visual object tracking has very important research value. Therefore, a deep learning based on robot vision tracking is evaluated. Based on the basic principles of target tracking and search principle, a deep learning algorithm for visual tracking is constructed, and finally, evaluated, and simulated. The results showed that the accuracy rate increased from 90.9% to 90.13% after the addition of channel attention mechanism module. Variance was reduced from 3.78% to 1.27%, with better stability. The EAO, accuracy, and robustness of the algorithm are better than those without significant region weighting strategy. The strategy of using the improved residual network SE-ResNet network to extract multiresolution features from the correlation filtering framework is effective and helpful to improve the tracking performance.
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