Objective: To achieve energy-saving and emission reduction in buildings, green building design is increasingly gaining attention. However, traditional design methods often rely heavily on the designer's experience, which complicates the consideration of multidimensional factors such as technical strategies and costs, thus limiting decision-making efficiency. Mining tacit knowledge to support green building design decisions and improve decision-making efficiency presents a significant challenge. Methods: This study proposes a two-stage intelligent decision-making model for energy-saving building strategies based on tacit knowledge. The first stage employs a case-based reasoning (CBR) model to determine energy-saving technical strategies. A case library containing 147 green-certified buildings provides reference strategies using attributes from the preliminary design phase, such as building type, structure, number of floors, height, orientation, shape coefficient, floor area, and green certification level. Cosine similarity helps retrieve relevant cases and identify technical strategies like window-to-wall ratios, heat transfer coefficients of the building envelope, heat pump loads, and renewable energy use. The second stage involves an incremental cost prediction model that uses machine learning algorithms. A 2∶8 split of the case library into test and training sets enables comparison across four machine learning algorithms: artificial neural network, extreme gradient boosting (XGBoost), support vector machine, and random forest. Each model's prediction accuracy, precision, and F₁ score (the harmonic mean of precision and recall) are evaluated. The model takes the technical strategies identified in the first stage and the known information from the preliminary design phase as input feature parameters. The import_plot module analyzes feature importance to eliminate redundant features. The two-stage model is validated on buildings from regions with hot summers and cold winters. Results: Findings indicate the following: (1) The CBR model effectively identifies and reuses the most similar energy-saving technical strategies, thereby improving decision-making efficiency. Most target cases achieve a similarity greater than 0.8 in the case library. (2) Among the machine learning models, the XGBoost-based incremental cost prediction model exhibits the highest accuracy, achieving 72.41%. (3) By applying the synthetic minority oversampling technique to balance samples and remove outliers, the prediction accuracies for four types of costs reach approximately 70%. However, the prediction accuracy for the fifth type of incremental cost is lower owing to varying owner preferences and requirements. Conclusions: The proposed two-stage intelligent decision-making model successfully integrates the CBR model with machine learning algorithms. The proposed model optimizes the use of limited known information available during the preliminary design stage to predict both technical strategies and incremental costs. This model enhances the scientific rigor and efficiency of energy-saving decision-making, providing significant support for green building design.