There is a problem in that the traveling wave head calibration method of a distribution network is easily affected by noise and wave head distortion. Thus a fault traveling wave calibration method based on the full-band characteristics of the traveling wave is proposed. From the singularities of the high frequency component and the noise immunity of the mid-low frequency component of the traveling wave, the full frequency component of the traveling wave is used to calibrate the traveling wave, and the advantages of using the full frequency component of the traveling wave in different working conditions are analyzed. Then, a convolutional neural network (CNN) based on a target detection model is designed and built. The full-band component of the traveling wave is taken as the feature input, and the wave head features of the traveling wave signal are extracted using a one-dimensional convolution kernel. Finally, the feature pyramid network and path aggregation network structure are combined to integrate the high, middle and low band features of the traveling wave head, and the accurate calibration of the arrival time of the traveling wave is realized. Compared with the traditional method, the proposed method has stronger adaptability in the case of short line and strong noise, and can also realize wave head calibration in the weak fault traveling wave scenario, and has good field application.