The urban energy system is greatly dependent on the District Heating System (DHS). However, many difficulties with regulation and control are caused by its large scale and numerous coupling variables. Additionally, reliance on manual experience means it can be challenging to guarantee heating comfort and effectiveness in the regulation of DHS. This paper proposes a data-driven temperature response prediction model to predict secondary loop supply temperature based on the heating substation’s historical operating status, valve opening degree, weather conditions, etc. Further, the XGBoost model was established in this article with different input and prediction steps. The results show that the XGBoost model with 72 input steps and 24 prediction steps has better performance. As an application example, the model was applied to an urban central heating system. Based on this data-driven model, different operation strategies on primary loop valve opening are compared for temperature response analysis. Operators can check the temperature responses of different valve control strategies before being applied. This paper guides the regulation behavior of the DHS, which is of great significance for the operation of the actual DHS.