Aiming at the dual bottlenecks of insufficient feature extraction and difficulties in hyperparameter optimization in traditional methods for lithium-ion battery state of health prediction, this paper proposes a deep learning hybrid model (SSA-DA-CNN-LSTM) integrating a dual-attention mechanism and Sparrow Search Algorithm (SSA) optimization. The model encompasses two core innovations. First, after extracting local features using a Convolutional Neural Network (CNN), feature and temporal dual-attention modules are introduced to adaptively quantify weights and focus on core degradation features, overcoming the defect of information loss in long sequences. Second, the SSA is utilized to automatically conduct global optimization for key network hyperparameters, completely avoiding the blindness of manual parameter tuning and the risk of falling into local optima. Experiments based on the NASA dataset show that, benefiting from precise feature focusing and global parameter optimization, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of this model in full-lifecycle SOH prediction are significantly lower than those of traditional baseline models. The model not only achieves high-precision tracking of global nonlinear degradation but also exhibits strong robustness when dealing with complex local features such as capacity regeneration, providing reliable algorithmic support for next-generation intelligent Battery Management Systems (BMSs).