In cloud computing platforms， multidimensional resource features of application services influence data center energy consumption significantly. Extracting key energy consumption indicators through feature selection techniques serves as an effective means to build accurate prediction models. The existing studies fail to adequately integrate feature interpretability with global optimal search capability， resulting in issues such as high redundancy， low prediction accuracy， or lack of decision interpretability in selected features. To address these problems， a two-stage energy consumption feature selection method based on tree-based models was proposed. In the first stage， SHapley Additive exPlanations for Tree-based models （TreeSHAP） was used to quantify marginal contribution of features and realize highly transparent elimination of redundant features. In the second stage， rapid convergence of Ant Colony Optimization （ACO） algorithm was combined with global search capability of Gravitational Search Algorithm （GSA） to localize the optimal feature combination in the reduced feature space. Through the two stages working collaboratively： the low-dimensional interpretable space filtered by TreeSHAP was used to provide a foundation for ACO-GSA， with computational complexity reduced significantly， while key feature combinations were identified by ACO-GSA efficiently through a hybrid search strategy. By comparing different feature selection methods and validating the performance of the feature subsets on multiple prediction models， a comprehensive evaluation was conducted in terms of feature dimensionality， prediction accuracy， and generalization performance. Experimental results demonstrate that the proposed method reduces the feature subset dimensionality by 58.3% compared to Least Absolute Shrinkage and Selection Operator （Lasso） and improves the prediction accuracy of the feature subset by 9.1% compared to ACO on the University of Melbourne cloud dataset. It can be seen that the proposed method outperforms comparative methods in feature compactness， prediction accuracy， and generalization reliability， verifying its effectiveness.