In order to solve the problem of printing failure caused by abnormal conditions such as plug,broken wire and warping in 3D printing process,a detection platform was built and an improved YOLOv5 algorithm with Xception was proposed to complete real-time anomaly detection,achieving the goal of timely processing and printing success rate improvement.The YOLOv5 algorithm was reconstructed by improving the head,trunk and bottleneck block of YOLO algorithm,improving the identification frame rate and reducing the parametes.Then the output part was improved so that the abnormal images with similar features were collected and input into Xception algorithm to improve the accuracy of abnormal recognition and classification.Finally,the Qt cross-platform development framework was used to design a printing abnormal diagnostic system human-computer interaction interface software.The results show that the accuracy rate of the improved fusion algorithms in self-built 3D printing abnormal data set recognition is 88.75%,which is 3.22% higher than the original YOLOv5 algorithm,and the average recognition frame rate is 28 f/s,which is increased by 40.0%.It can meet the actual printing recognition accuracy and real-time requirements.