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To reduce the operating energy consumption of air-conditioning systems and optimize the load distribution of chillers, a multi-strategy improved tuna swarm optimization algorithm (MSTSO) is first proposed, which introduces a golden sine foraging mechanism and non-linear inertia weights to enhance the algorithm's ability to locate the optimal solution globally and uses a honey badger random search strategy to give the algorithm stronger performance to jump out of the local optimum. Then, using a bi-directional long short-term memory (BiLSTM) network to build an energy efficiency prediction model and use the MSTSO algorithm to optimize its initial parameters to obtain the best training results. Finally, the BiLSTM-MSTSO energy consumption optimization model is further proposed to reasonably allocate and optimize the load of multi-chillers. The experimental results show that the optimized BiLSTM prediction model has higher prediction accuracy and the MSTSO algorithm can reduce energy consumption and maximize the operating efficiency of chillers compared to other intelligent optimization algorithms. Therefore, the BiLSTM-MSTSO intelligent model can be used to predict and optimize the energy consumption of multi-chillers.