In the context of global timber supply chains facing policy adjustments, resource fluctuations, and market uncertainties, this study focuses on the resilience of the Sino-Russian timber supply chain. A system dynamics (SD) model is developed to analyze the dynamic evolution of the key segments. By integrating the entropy weight–TOPSIS method, the research quantitatively assesses overall supply chain resilience by synthesizing data from four capability dimensions—Russian logistics and transportation capability, Russian primary wood processing capability, Sino-Russian timber import–export capability, and Heilongjiang furniture sales capability—over the 2017–2033 period. Results indicate a “first decline, then rise” trajectory for resilience, with a minimum normalized resilience index of 0.1549 recorded in 2021, followed by a gradual recovery and sustained strengthening thereafter. Among evaluated segments, Russian logistics demonstrates the strongest short-term shock resistance (36.2% reduction in minimum resilience), while Heilongjiang’s sales segment exhibits optimal long-term recoverability (the normalized resilience index increased by an average of 0.0363 units per year during the recovery phase). Based on these findings, a “short-term logistics enhancement–long-term demand-driven” strategy is proposed to improve resilience, providing actionable insights for the high-quality development of the Sino-Russian timber supply chain.

As a crucial component of the Belt and Road Initiative (BRI), China Railway Express (CR Express) plays a pivotal role in enhancing regional connectivity and economic integration. However, the systematic evaluation of CR Express node cities remains understudied, hindering the optimization of logistics networks and sustainable development goals. This study pioneers a data-driven approach by integrating multi-source geospatial data and advanced machine learning algorithms to develop a comprehensive evaluation framework spanning five critical dimensions: economic vitality, ecological sustainability, logistics capacity, network connectivity, and policy support. By comparing the evaluation performance of six machine learning models, an optimal decision-making model is identified, and the evaluation indicators are rigorously screened to provide robust decision-support for the establishment of CR Express assembly centers. The Random Forest model outperformed comparative algorithms with 99.5% prediction accuracy (8.33% higher than conventional classification models), particularly in handling multi-dimensional interactions between urban development factors. Feature importance analysis identified 11 decisive indicators from node city evaluation empirical indicators, where CR Express trade volume (weight = 0.1269), logistics hub classification (weight = 0.1091), and operational frequency (weight = 0.0980) emerged as the top three predictors. Spatial predictions highlight five strategic cities (Changsha, Wuhan, Shenyang, Jinan, Hefei) as prime candidates for CR Express assembly centers, providing actionable insights for national logistics planning under the BRI framework.