To improve the accuracy of State of Health (SOH) monitoring for lithium-ion batteries and prevent potential failures, this paper proposes a joint model based on Convolutional Neural Network (CNN) with enhanced Bidirectional Long Short-Term Memory (BiLSTM) for predicting battery SOH. Firstly, five highly correlated health factors related to battery aging are extracted as SOH features from the publicly available NASA battery dataset. Secondly, a joint network model is constructed where the CNN layer extracts spatial features from the battery charge/discharge characteristic data, the BiLSTM layer captures correlations among different features in the data, and an attention mechanism allocates weights to input variables. Finally, training and prediction are performed on both individual battery datasets and datasets of similar batteries. Simulation results demonstrate that the joint model estimates lithium battery SOH with an average absolute error distribution within 0.015, maximum absolute error and RMAE within 0.01, indicating high accuracy and good generality.