Improving the forecasting accuracy of heating load of district heating system (DHS) is an important method to realize intelligent heating and improve energy utilization efficiency. This paper proposes a combination heating load forecasting model based on Bayesian posterior estimation, Long Short Term Memory (LSTM) algorithm and eXtreme Gradient Boosting (XGBoost) algorithm. Initially, we confirm the input features based on the characteristics of heating load changes to construct the LSTM model and the XGBoost model respectively. Then, the Bayesian posterior estimation algorithm combined them to construct the LSTM-XGBoost combination model. The operating data of a heating station in Shijiazhuang from 2019 to 2020 was used for simulation validation, and the evaluation indicators of its results indicate that the Mean Absolute Percentage Error (MAPE) of the LSTM-XGBoost combination model based on Bayesian posterior estimation proposed is about 0.51%, which means the average deviation of heating load forecasting is about 0.51%. Comparing the proposed method with Recurrent Neural Network (RNN) model and Gate Recurrent Unit (GRU) model, and the result verified that the proposed method can effectively improve the accuracy of heating load forecasting.