As an innovative renewable and clean energy device, the proton exchange membrane fuel cell (PEMFC) holds immense market application value. PEMFCs are susceptible to water management faults during prolonged operation under complex and varying conditions. However, traditional fault diagnosis methods struggle to effectively extract key fault features from dynamically changing monitoring data. To address this, this study proposes a PEMFC fault diagnosis method based on a deep parallel residual neural network (DP-ResNet). This method initially processes the collected multi-source signals, such as current and voltage. Subsequently, a DP-ResNet is designed to overcome the limitation of residual networks in multi-scale feature extraction. Finally, the proposed algorithm is applied to a dataset of PEMFC water management faults under varying load conditions for diagnostic verification. Experimental results demonstrate that the proposed DP-ResNet model achieves a diagnostic accuracy of up to 99.46% for flooding faults in real PEMFC experimental datasets. Compared with traditional machine learning algorithms such as Decision-tree, GaussianNB, KNN, and CNN, the proposed method demonstrates superior feature extraction and diagnostic accuracy.