Accurate and effective battery state-of-health (SoH) monitoring is significant to guarantee the security and dependability of electrical equipment. However, adapting SoH estimation methods to diverse battery kinds and operating conditions is a challenge because of the intricate deterioration mechanisms of batteries. To solve the issue, in this article, a novel multi-input metabolic long short-term memory (MM-LSTM) framework is developed. A degradation state model is created with the LSTM network to describe the intricate deterioration mechanisms. To convey more information about battery aging, the capacity degradation, sample entropy of discharge voltage, and ohmic internal resistance increment are extracted as the inputs of the model. To estimate SoH with a few data, the metabolic mechanisms are introduced to update the inputs and reflect the latest developments in aging. The accuracy and robustness of the proposed MM-LSTM framework are verified in different aspects using two kinds of batteries, and the maximum estimation error of SoH is within 1.98%. The findings indicate that the MM-LSTM framework implements the transfer application of SoH estimate successfully, and the framework’s versatility has been proven.