The invention discloses a data center energy efficiency optimization method based on transfer learning in the field of data mining and machine learning. The method comprises a model training method and a model reasoning decision-making method. A basic model is pre-trained based on Base Model by using data samples of all units; according to the method, hidden layer parameters learned by a basic model are migrated to List-wiseModel of each unit, and fine adjustment is carried out by using a relatively low learning rate, so that the problem of missing of part of unit samples is solved, and the generalization ability of the model is improved; by designing a multi-task learning model and adding rank constraint to multi-target loss, the problems of overfitting and model high variance caused by noise are solved, and a sample pair is selected in a random and periodic sliding window mode to improve the convergence rate of the model; the optimal unit control parameters are obtained by using theoptimal linear search of the energy consumption prediction task, the control parameters are sorted by using the rank prediction task sorting model, and the optimal control parameters are comprehensively selected, so that the accuracy and robustness of the optimal control parameters are improved.