Time series with change-points always are important topics in economics, engineering and statistics, which have been widely applied in financial, meteorological, industrial and other fields. In this paper, we study the statistical inference of the AR(1) models with single change-point. For the AR(1) models with single change-point, we provide the estimators and the consistency condition of the autocorrelation coefficient estimators based on maximum likelihood (quasi-likelihood) methods. Under the provided sufficient conditions, we establish that the asymptotic distribution of the estimators, the hypothesis test on whether there is a change-point in the models and the increment of autoregressive coefficients are discussed. Finally, some simulation results and the analysis of the daily trading data of Shanghai Composite Index show the effectiveness of the proposed theories and methods.