State of health (SOH) predictions are critical for battery management systems. Due to the complexity of battery health assessment modeling and large prediction errors, accurate SOH prediction still needs to be improved. In this paper, Improved sparrow search algorithm (ISSA)-Convolutional neural network (CNN)-Bidirectional Gated Recurrent Unit (BiGRU)Attention mechanism for lithium battery health status assessment is proposed by combining capacity increment analysis (ICA) and differential voltage analysis (DVA) methods. Firstly, the capacity increment (IC) curve and differential voltage (DV) curve are processed by Gaussian filtering to avoid the influence of noise. A set of new battery aging features were extracted from the filtered IC and DV curves through the center for advanced life cycle engineering Advanced Life Cycle Engineering (CALCE) data processing. The Pearson correlation coefficient between the four aging features and SOH was above 0.9. ISSA-CNN-BiGRU-Attention method was used to construct a prediction model of battery SOH, and the proposed method was compared with CNN, BiGRU, CNN-BIGRU and other methods. Experimental results showed that the maximum MAE and RMSE errors of the proposed method were 0.005 44 and 0.007 17, respectively. Compared with other models, it has excellent robustness and accuracy, and has better practical use value.