A fault diagnosis method for electro-hydraulic servo valve based on multi-source signal fusion and hybrid attention mechanism was analyzed in order to improve the diagnostic accuracy and robustness for concealed leakage and blockage fault in electro-hydraulic servo valve under heavy-load, time-varying-speed and strong-noise condition, aiming to enhance the capability of the model for weak fault feature representation and recognition. A dual-channel multiscale Transformer fusion network for pressure and control signal, named DC-MTF-Net, was constructed oriented toward engineering application scenario. A convolutional attention module was employed to enhance multiscale temporal feature extraction, while cross-attention gated fusion was introduced to realize adaptive weighting of multi-source feature. A two-stage learning framework combining autoencoder pretraining and classification fine-tuning was adopted to improve feature representation and generalization capability in complex production environment. Multi-fault experiments conducted on an electro-hydraulic servo valve test platform under heavy-load and time-varying-speed condition showed that the proposed method improved diagnostic accuracy by at least 6%, compared with baseline models. Higher stability and robustness were demonstrated in comparative experiments involving single-channel diagnosis and various noise disturbance, verifying the effectiveness of the method in engineering application.