[Objective] For armature winding inter-turn short circuit fault diagnosis in aerospace generators, this paper proposes a diagnostic method based on convolutional neural network (CNN) with squeeze-excitation (SE) attention mechanism and bidirectional long short-term memory (BiLSTM). It aims to enhance the effectiveness and robustness of armature winding fault diagnosis. [Methods] Firstly, an equivalent analytical model for armature winding inter-turn short circuit fault diagnosis in a doubly salient electromagnetic generator (DSEG) was established, and the vibration signal on the surface of the generator casing was adopted as a characteristic signal of the fault. Theoretical derivations of the expressions for the air gap magnetic flux density and electromagnetic force, which influenced vibration characteristics, were carried out. Secondly, the impact of electromagnetic force on vibration characteristics under various short circuits was analyzed using finite element simulation in Workbench software. Finally, vibration signals were collected and used as actual experimental data to extract relevant fault features. These extracted features were put into the CNN-SE-BiLSTM for efficient fault diagnosis. Among this, the CNN and BiLSTM networks effectively extracted local features and features in time series from vibration signals, while the SE attention mechanism optimized the feature weight distribution by selectively amplifying relevant diagnostic features, significantly enhancing the model’s fault diagnosis capability. [Results] The experimental results showed that the proposed CNN-SE-BiLSTM-based method achieved a diagnostic accuracy of over 99% for inter-turn short circuit faults in armature windings under various working conditions. Moreover, it still exhibited strong diagnostic performance in noisy environments. Compared to traditional diagnostic methods, the proposed method not only demonstrated strong anti-interference ability, high fault recognition accuracy, and fast diagnostic speed under various rotational speeds and load conditions, validating its applicability in aerospace generators with complex operating conditions. [Conclusion] The CNN-SE-BiLSTM diagnostic approach effectively improves the fault diagnosis efficiency and accuracy of DSEG systems. By combining CNN for spatial feature extraction, SE attention mechanism for selective feature weighting, and BiLSTM for capturing comprehensive temporal information, this method provides a streamlined and highly effective solution for inter-turn short-circuit fault diagnosis.