In view of the current lack of effective detection methods for debonding defects in in-service rubber-lined pipes, as well as low detection efficiency and accuracy, based on the basic principle of ultrasonic pulse echo method, a scanning and probe clamping device suitable for ultrasonic detection of cylindrical rubber-lined pipes was designed, and a corresponding ultrasonic detection experimental system was established.Various interference factors that affect ultrasound echo signals in practical applications have been analyzed, and a binary classification model for ultrasound echo signals based on one-dimensional convolutional neural network (CNN) has been specifically constructed. Through experiments and comparison with traditional ultrasonic detection defect recognition methods, the results show that the established ultrasonic detection system and one-dimensional CNN model can achieve more accurate identification of debonding defects even in the presence of multiple interference factors, with an accuracy rate of 96.22%. This provides an effective method and means for the automated detection and recognition of debonding defects in in-service rubber-lined pipes.