The state of health (SOH) of lithium-ion batteries is an important reference indicator for the reliable operation of lithium-ion batteries. To improve the accuracy of the battery state of health detection, a method for the lithium batteries state of health detection based on the CNN-BiLSTM network is proposed. This method uses CALCE lithium-ion battery capacity decay data set, extracts battery health indicator (HI) as the model input data, and uses grey relational analysis (GRA) to verify the rationality of HI selection. Convolutional neural networks (CNN) and bi-directional long short-term memory (BiLSTM) are used to construct network models to predict battery capacity and to detect the health status of lithium-ion batteries. The results show that the method has 1.79% RMSE and 1.3% MAE for SOH detection, with high accuracy and reliability.