To address the challenges of intelligent diagnosis for open-circuit faults in power electronic inverters, such as the lack of actual fault samples and the issue of varying characteristic adaptability, a set of optimization methods was proposed from two key intelligent elements: data and algorithm, to support the practical applications of intelligent diagnosis for open-circuit faults in power electronic inverters. For the data element, a fault sample amplification method based on inverters′ characteristics was proposed, which finds out the minimum number of practical samples required for model training. For the algorithm element, an attention-enhanced method and a frequency points adaptive training method for the diagnosis model were proposed, which significantly improve model training effectiveness and diagnosis accuracy under wide-frequency inverter operation. The effectiveness of the proposed optimization methods for the intelligent elements was validated by experiments.