Abstract: The present disclosure provides a method and system for diagnosing a leakage in a hydrogen system for a vehicle, an electronic device, and a storage medium. The method includes: obtaining data of a hydrogen cylinder gas pressure of a fuel cell vehicle; separately performing Gramian angular field transformation and Markov transition field transformation on the pressure data, to obtain static and dynamic feature information; performing, by a static feature LeNet neural network, recognition based on the static feature information, to obtain a probability output of the static feature LeNet neural network; performing, by a dynamic feature LeNet neural network, recognition based on the dynamic feature information, to obtain a probability output of the dynamic feature LeNet neural network; and performing fusion through a Dempster-Shafer (D-S) evidence theory based on the probability output of the static and dynamic feature LeNet neural network, to obtain an excellent hydrogen leakage diagnosis result.