The saturable reactor is one of the core components of the thyristor valve, which plays a role in suppressing current changes and balancing voltage distribution. Due to long-term operation under complex working conditions, the vibration of the reactor can cause the core to loosen. To detect faults in time, a fault diagnosis method is proposed for loose cores in saturable reactors of thyristor valves based on time-frequency analysis of vibration signals and convolutional neural networks. The two-dimensional time-frequency spectrum of the vibration signal is obtained using the short-time Fourier transform method. A convolutional neural network model structure is designed to extract time-frequency spectrum features and diagnose different levels of core looseness faults through learning and training. An experiment was carried out to collect core fault information under variable working conditions. The effectiveness of the method proposed in this paper was verified based on experimental data validation. Through comparative experiments, it was found that the proposed method is superior to existing fault diagnosis methods.