ing at classification of carbon fiber reinforced polymer (CFRP) defect types, an ultrasonic one-dimensional convolutional neural network (U-1DCNN) is proposed and the Bayesian optimization algorithm is used to optimize hyperparameters. By automatically extracting the features of ultrasonic A-Scan signals, three defect types, i.e., delamination, gas cavity, and non-defect, are automatically distinguished. First, a dataset is constructed by collecting ultrasonic A-Scan signals. Then, multi-convolutional blocks are used to simultaneously extract as well as enhance the diversity of extracted features. Subsequently, one-dimensional residual units are stacked and connected, simplifying the training of the network while further extracting the features. The learning rate and momentum parameter of stochastic gradient descent of the network are optimized by Bayesian optimization algorithm. Finally, nonlinear mapping of the A-Scan signals and defects is realized. Experiment results show that U-1DCNN can recognize CFRP defects by automatically extracting features, with the accuracy reaching 99.50%.The recognition speed of U-1DCNN is faster than the two-dimensional CNN method, which is advantageous for defect detection.