ZPW-2000A track circuit plays an important role in the process of ensuring the safety of train operation, once the failure will cause unpredictable consequences. Therefore, fault prediction of track circuit is of great significance. In this paper, an improved grey GM(1,1) prediction model is proposed to predict and analyze the red band phenomenon of track circuit, which solves the problems of low prediction accuracy and certain error of the traditional grey GM(1,1) prediction model. By introducing the weakening factor to reduce the prediction error caused by the original data fluctuation, and using the rectangle method to optimize the background weight of the traditional model, the optimal background parameters under the constraints were obtained based on the genetic algorithm, and the improved GM(1,1) prediction model was obtained. The performance of the improved prediction model is verified by combining the rail outlet voltage data collected from the signal workshop of railway bureau. The results show that compared with the traditional grey GM(1,1) model, the average relative error of the improved model is reduced by 28.3%, and the improved model has higher prediction accuracy and practical value.