Abstract: To enhance the accuracy of neural network models in predicting the remaining useful life of lithium-ion batteries, this paper proposed a prediction method based on the Attention-GRU (Gated Recurrent Unit) model. Firstly, based on the Random Walk battery data publicly available from NASA, health factors that significantly correlated with battery capacity decay were extracted from direct measurement data such as battery voltage, current, and time, and the correlation between these health factors and battery capacity was calculated. Secondly, an Attention-GRU model was constructed to learn the patterns of change in health factors, allocate attention weights based on correlation, obtain attention vectors, and adjust the output of the hidden layer. Finally, predictions for the remaining useful life of the battery were made, and predictions for the B05 series of batteries were added to explore the model's generalization capabilities. Experimental results indicated that when using the Attention-GRU model to predict the Random Walk batteries, the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) were around 0.015, and for the B05 series batteries, the MAE and RMSE were below 0.01. The prediction accuracy was superior to comparative methods, demonstrating high precision and good generalization ability.