In this study,we aim to expedite the assessment of direct economic losses induced by earthquakes,focusing on China's western region. We employ a random forest machine learning regression algorithm for this purpose. Leveraging earthquake damage data spanning from 1993 to 2017,in conjunction with economic and seismic design data from various years,we train and test the model following feature selection and parameter optimization steps. The findings reveal that the optimized random forest model yields superior evaluation outcomes while reducing the model's input features. Specifically,the evaluation model achieves an R² value of 0.86 under the data preprocessing method involving the deletion of missing feature samples,surpassing the evaluation model's performance under the median filling missing feature data preprocessing approach. This optimized model proves more suitable for assessing direct economic losses attributable to earthquakes. Validation using real world examples demonstrates that the evaluation results derived from this model align closely with actual economic losses,underscoring its utility in providing decision support for earthquake relief efforts.