It is essential to accurately predict the state of health (SOH) of lithium-ion batteries. Aiming at challenges such as differences in degradation mechanisms at different stages of a single battery cycle and incomplete data acquisition in practical utilization scenarios, a lithium-ion battery SOH estimation method based on Involution-Vision Transformer (IViT) is proposed. Features that can effectively characterize the degradation information of lithium-ion batteries are automatically extracted from the voltage-time profile, weights are adaptively assigned at different positions using the Involution module, and Vision Transformer is used to learn the high-level feature representations at different stages and capture the global dependencies. The experimental results show that the prediction error of IVIT is around 0.5%, and the error is only around 2% when the overall data is missing 50%, proving the effectiveness and stability of the proposed method.