Because the prediction error of photovoltaic point can not be avoided, interval prediction can be used to describe the uncertainty of photovoltaic more accurately, which can provide guidance for the decision-making of power system, but the existing research methods can not fully mine the physical change process of photovoltaic power. A prediction framework of intraday photovoltaic output interval considering the spatiotemporal-conditional dependence of prediction error is proposed. Firstly, the prediction error considering time dependence is obtained by appearance similarity update （ASU) model, then the prediction error considering spatial dependence is obtained by long short-term memory （LSTM) model and spatial correlation analysis, and the prediction output is modified. Finally, the interval prediction under different confidences is obtained according to the conditional dependence of the error. The effect of the whole framework has been verified in a photovoltaic electric field in Xinjiang, and its root mean square error can be reduced by more than 3%. At the same time, the interval prediction effect considering the spatiotemporal-conditional dependence of the updated prediction error has been improved, which verifies the effectiveness and feasibility of the proposed method.