This study proposes an algorithm for detecting metal damage in combination with information entropy and low-rank tensor analysis to address the problems of low automation degree and recognition accuracy in the research of damage identification of metal parts. First, the image is denoised using the difference method, median filtering, and Fourier filtering. Second, according to the obvious difference between the damage of the metal part and its surroundings, the information entropy edge detection is used to obtain the edge information. At last, the low-rank tensor method is used to analyze the difference entropy and the weight entropy matrix to extract damage, and it is compared with other algorithms. The experimental results show that the algorithm can effectively and quickly identify metal damage with few noise points. The effective accuracy of the algorithm is higher than 80% with good robustness, which is higher than that of traditional algorithms.