State of health (SOH) prediction, as one of the key functions of lithium ion battery management system (BMS), is of great significance to ensure the safe and reliable operation of batteries and reduce the maintenance cost of battery system. In order to improve the prediction accuracy of lithium battery SOH, a SOH prediction method based on improved ant-lion optimization algorithm and support vector regression (IALO-SVR) is proposed. Firstly, the characteristic factors related to battery capacity are extracted from the battery charging data, and the correlation analysis is carried out. The three features with high correlation are selected as the model feature inputs, and then the sample data is imported. The key parameters of SVR model are optimized by the IALO algorithm, and the final prediction model is established. Compared with the existing GA-SVR and IPSO-SVR, the results show that IALO-SVR method NASA has higher prediction accuracy and fitting degree, and the prediction error is basically kept within 1%, which verifies the feasibility of the prediction method.