The uncertain time series (UTS) is a sequence of uncertain observations in chronological order. The uncertain autoregressive (UAR) model is one of the basic UTS models believes the uncertain time series value relies mainly on it’s historical values linearly. This paper proposes uncertain interrupted time series (UITS) models aiming at analysing time series datas with large-scale interventions on the base of uncertain autoregressive model. The UITS model can reflect the effect of an intervention and makes prediction about the future in the presence of intervention. Three types of uncertain interrupted time series models are introduced in this paper. In addition, residual analysis and prediction intervals are also proposed. Finally, some numerical examples are given.