In the welding process, a sensor system for real-time detection is difficult to realize. To address this problem, a robust identification algorithm of weld feature points based on kernel correlation filter tracking is proposed. First, based on the traditional kernel correlation filtering theory, an occlusion discrimination mechanism is added, and the learning rate of the model is dynamically adjusted to avoid introducing welding noise. Subsequently, the accuracy of the kernel correlation filter is further improved, and the sub-pixel accuracy alignment method is proposed, enabling target positioning accuracy at the sub-pixel level. Comparative experimental results show that the performance of the improved recognition algorithm is greatly improved, demonstrating superiority over 11 common visual identification algorithms. For 1.1-million-pixel images, the average positioning error of the proposed algorithm is only 2.36 pixels, and the operation speed reaches 40 frame/s, which fully meets the needs of real-time weld detection. Practical welding experiments further prove the effectiveness of the proposed algorithm.