With the development of the new energy industry, how to deal with more and more retired batteries has become an urgent problem. Lithium iron phosphate batteries are widely used in automotive and energy storage scenarios due to the advantages of high energy density and safety. It is one of the mainstreams of existing retired batteries. The secondary utilization scenario of retired LiFePO4 batteries is evaluated based on the health status of the battery, internal resistance, and other states. But, this process consumes a lot of time. In this article, we propose to use the frequency domain characteristics of the voltage during the pulse process as the health features for estimating the health state. Then, the random forest regression algorithm is used to achieve a fast estimation of the health state, which greatly shortens the time for the sorting of decommissioned batteries. On this basis, this article proposes the use of an abnormal parameter identification method based on Gaussian distribution to evaluate the abnormal internal resistance of retired lithium iron phosphate batteries. Through experimental evaluation, the maximum error of health state estimation in the selected 15 LiFePO4 batteries is 6% , and the proposed method can effectively screen out the retired LiFePO4 batteries whose internal resistance does not match with SOH.