The State of Health (SOH) and Remaining Useful Life (RUL) of batteries are important technical indicators for evaluating the health level and remaining lifespan of batteries. The estimation of SOH and RUL is an important component of the battery management system and the foundation for achieving intelligent monitoring and scientific operation of the battery management system.Electrochemical Impedance Spectroscopy (EIS) is a testing method used to characterize the internal electrochemical processes of batteries, which has the advantages of high accuracy and non-invasive damage. Various studies have shown that there are some inherent connections between battery impedance spectroscopy (EIS) and the SOH and RUL of batteries, making it a research hotspot in the field of electrochemistry. Based on EIS to predict SOH and RUL,traditional machine learning methods are relatively mature, but there are still limitations in prediction accuracy and stability, making it difficult to fully explore the decay patterns of batteries. Therefore, it is necessary to combine with methods such as deep learning to improve prediction performance. Dimensionality reduction models and various deep learning models were introduced into the fields of SOH and RUL prediction, and the models were effectively combined, achieving good results. The real and imaginary data was arranged corresponding to all frequencies of the EIS as frequency features. Firstly, the Principal Component Analysis (PCA) model was used to reduce the dimensionality of the EIS values,10 refined principal components were extracted, and then the Convolutional Neural Network (CNN) model was used to extract the spatial features of the EIS, Using the Bidirectional Long Short Term Memory (BiLSTM) model to extract the variation patterns of EIS time series, using the Attention mechanism to further select important parts of the spatiotemporal features of EIS data, and SOH and RUL were jointly predicted. Experiments on test data show that the Root Mean Square Error (RMSE) of SOH prediction reaches 0.1468, and the mean square error of RUL prediction reaches 2.6145, both of which are better than traditional methods.