In order to realize the digital detection and traceability of riveting quality,a digital detection method for riveting quality based on machine learning was proposed.Based on the images of riveted part captured by CCD cameras,median filtering,Canny edge detection,image morphology processing were used to detect the cracks and extract the feature information.A riveting quality detection model was established based on the improved particle swarm optimization least squares support vector machine algorithm,and the model was tested by using the detection samples.The quality tracing of unqualified products was carried out, and the expert system was used to determine the causes of defects.The prototype system was tested through the inspection samples and validated in a certain type of aircraft assembly workshop.The results show that the designed system detection accuracy rate reaches 96%,by which the efficiency of riveting quality testing can be improved,the testing standards can be unified,and the labor can be reduced.