To meet the demand for rapid detection of battery health status in the process of retired power battery recycling, this paper takes soft pack lithium iron phosphate batteries as the research object and proposes a rapid detection method of lithium battery health status based on infrared thermal imaging. By changing the battery charging and discharging current multipliers, the temperature changes of batteries with different aging degrees during the discharge process are studied, and the infrared thermographic video during the discharge process is collected to establish the correspondence between the battery health state and the infrared thermographic features, which is used as the health factor for battery health state detection; an improved video recognition algorithm based on SlowFast-LSTM deep learning network model is constructed for battery health state detection. The improved video recognition algorithm achieves an average recognition rate of 80.78% for the six categories of battery health state 0~40%, 40%~50%, 50%~60%, 60%~70%, 70%~80% and 80%~100%, and a single battery detection time of 3 minutes, which enables fast detection of battery health state.