In the field of the wood industry, the competitive effect caused by interregional resource differences and the linkage effect generated by industrial chain synergy profoundly affects the development direction of the industry in each province. Based on China’s input–output table from 2002 to 2017, this paper constructs an industrial comparative advantage measurement model using location entropy and finds that the industrial agglomeration advantage of the wood products industry in Heilongjiang Province is remarkable in the national context, and that it had already caught up with 79% of the provinces in 2017; we analyze the industrial characteristics of the wood products industry in Heilongjiang Province through the upstream and downstream degrees, the backward and forward correlation coefficients. The findings indicate that the average value of the downstream degree is 28.57% higher than the average value of the upstream degree in Heilongjiang Province, and the industry association mode has shifted from “demand-pull” to “supply-led”. Therefore, the timber industry in Heilongjiang Province has the capability to transform its resource advantage into a competitive edge across the entire industrial chain. Meanwhile, its sensitivity to economic fluctuations in various provinces is increasing significantly. However, there are still notable shortcomings: insufficient capacity to expand the terminal market and integrate the downstream segments of the industrial 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.