In this study, a method based on machine vision is proposed for the rapid nondestructive detection of laser surface modification in copper-chromium alloy. Surface morphology images of the specimen are collected, and the visual salient regions are segmented from the background by applying the adaptive thresholding method are extracted. Additionally, based on geometric moments, the characteristics of the connected domain with spatial transformation invariance. According to the laser energy input, four basic modification states are defined, and a support vector machine is trained to determine the modification quality. Writing scripts in MATLAB language, the results show that it takes about 45 s for feature extraction and model training. Moreover, the recognition speed is about 5×10 6 pixel/s, and the recognition accuracy is about 97.0%. Based on the detection results, the corresponding process parameters can be optimized. Furthermore, the method is not sensitive to light and other detection environment factors, thereby achieving the requirement of rapid and nondestructive detection of laser surface modification quality, which has a certain significance for the optimization of process parameters.