The evolution of performance parameter states of civil aviation engines presents multi-stage characteristics， and the current evaluation methods for performance parameters cannot effectively use the big data of state monitoring， or accurately identify their influence on the performance of civil aviation engines. Therefore， a new reliability evaluation model considering the phase correlation of dynamic evolution of civil aviation engine performance parameter states is proposed. The model uses dynamic Wiener process to characterize the states of the performance parameters of civil aviation engines according to the phase characteristics of the change point， and the joint probability distribution function model of the phased dynamic Wiener process is established by using Copula function. Monte Carlo simulation sampling method is used to evaluate the reliability based on the mathematical property of the first reach threshold of the evolution of performance parameter states. The actual condition monitoring data of airlines are used to verify the superiority of the proposed model， and the results show that compared with the model without considering the correlation between evolution stages， the average error of the proposed model for evaluating the state reliability of the performance parameters of civil aviation engines is reduced by about 12.9%.