At present, the relay protection states monitoring models use the static fault probability value to predict the equipment failure rate, which fails to consider the dynamic impact of equipment aging and maintenance, and the prediction results are unreliable. Therefore, a Markov chain state prediction method based on three parameters Weibull distribution dynamic optimization is proposed in this paper. First, the grey model-particle swarm support vector machine algorithm is used to calculate the more accurate failure rate function of relay protection equipment, and then it is used to dynamically modify the transition probability between each operation state in the Markov chain, and finally deduce the future operation state of the line protection. The simulation result shows that, the failure rate function solved in this paper has higher calculation accuracy than the function solved by the traditional method, and the dynamic optimization Markov chain model realizes the dynamic quantitative treatment of equipment aging and maintenance. The calculation results of states transition probabilities accord with the equipment operation conditions, and can effectively predict the operation state at any time within the specified operation life of the equipment. It has certain guiding significance for the optimization of protection maintenance strategy. This work is supported by the National Natural Science Foundation of China (No. 51877084)