In the process of using electromagnetic induction method to diagnose the breakpoint of the grounding grid of the tower, aiming at the error caused by manual diagnosis, this paper proposes a diagnosis model based on one dimensional-convolutional neural network (1D-CNN), the diagnosis model takes the one-dimensional magnetic field data directly above the grounding grid as input, and outputs the number and location of breakpoint faults through a deep neural network. This paper firstly verified the effectiveness of electromagnetic induction method in the diagnosis of tower grounding grid breakpoints through experiment, then a magnetic field breakpoint fault dataset was established and a 1D-CNN diagnosis model was trained. In the diagnostic accuracy verification experiment, the diagnostic model reached 97. 50% diagnostic accuracy on 40 faulty magnetic field samples, showing good generalization. The comparison experiment of the diagnosis effect shows that the AUC value of the 1D-CNN diagnosis model reaches 0. 951, the average recognition rate of various faults in three random trainings reaches 92. 08%, and the average test set accuracy in 15 trainings reaches 94. 30%. and the average training time per generation is 0. 875 0 s, which has obvious advantages over DNN and RNN in various indicators.