This paper uses a bi-directional long short-term memory (BiLSTM) neural network model to solve the state of health (SOH) and remaining useful life (RUL) traditional prediction methods of lithium batteries. The accuracy of traditional prediction methods is low. The problem. Firstly, extract the capacity data of the national aeronautics and space administration (NASA) lithium battery, convert the capacity data into SOH data and use it as input data; secondly, establish a two-layer BiLSTM neural network and use the Nadam optimization function to dynamically adjust learning rate; Then, the lithium battery data is analyzed through the two-way long and short-term memory neural network model to establish the connection between battery capacity, SOH and RUL; finally, the fully connected layer outputs the estimated curve of the battery SOH to predict its remaining life. Prediction experiments with NASA data show that the RUL prediction error of the BiLSTM neural network is stable within 3, and the fit of the SOH prediction curve is stable at 94.211%-95.839%. The BiLSTM neural network has higher robustness and accuracy.