As distributed power sources, agricultural electrical equipment, and user-side energy storage are scaled up in rural distribution networks, agricultural integrated energy systems play a key role in promoting distributed energy accommodation and economic operation. However, the uncertainty of electricity load and the diversity of connected equipment pose challenges to system optimization and scheduling. To address this, this paper proposes an optimal integrated energy scheduling method for agricultural parks that takes short-term load forecasting results into account. Firstly, a prediction model based on improved Bayesian optimization and ensemble learning is established. Compared to individual base learners, this model can effectively enhance reliability and accuracy, thus achieving a robust description of the scheduling scheme. Then, Interruptible Load (IL) resources and a reward mechanism are introduced to shift peak loads and improve the economic operation of agricultural parks. Finally, simulation analysis is carried out based on an actual microgrid in an agricultural park in North China. Results indicate that the proposed method reduces electricity costs by 1.02%, validating its effectiveness and advancement.