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Accurately predicting the cooling load of buildings is crucial for energy-efficient control of air conditioning systems, therefore, an improved bald eagle search (BES) algorithm is proposed to optimize the short-term cooling load prediction model of gated recirculation units (GRU); firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to decompose the building cooling load data into components with different frequencies, and a random forest combined with the recursive feature elimination method is used to select the corresponding features for the components with different frequencies; the improved BES algorithm is used to optimize the GRU model. Finally, the improved BES algorithm is used to optimize the parameters of the GRU model, and the BES algorithm is improved to address the shortcomings of the BES algorithm, by introducing the Sobol sequence to initialize the population, adopting the nonlinear control factor to balance the search capability of the BES algorithm, and the adaptive t-distribution strategy to enhance the algorithm's ability to find the optimal; the experimental results show that compared to the GRU and the optimized GRU, the optimized GRU is more efficient and effective than the optimized GRU. The experimental results show that compared with GRU and GRU with improved BES algorithm, the root mean square error of the proposed prediction model decreases by 34.27, 22.41, the average percentage error decreases by 2.72%, 2.63%, and the average absolute error decreases by 27.25, 25.26; compared with other prediction models, the proposed prediction model has a higher prediction accuracy, which is more advantageous in practical engineering applications.