At present, the detection of the yield strength of cold-rolled strip steel mainly depends on the damage detection, which greatly increases the detection cost. In this paper, the BP neural network is introduced into the yield strength prediction of cold rolled strip steel based on pulse eddy current. Firstly, the time domain and frequency domain characteristics of the pulse eddy current response signal are extracted. The stability of the characteristics of each pulse eddy current signal is analyzed, and the BP neural network model for signal characteristics and material yield strength is established, and the yield strength of the material is predicted using the established model. Experiments show that yield strength prediction error is 6% or less using the BP neural network to predict the yield strength of cold-rolled strip steel. This method has certain practical value for reducing the detection cost of industrial production and improving the detection efficiency.