Under the advancing urban–rural integration strategy, last-mile logistics, and their spatial accessibility, have become key indicators for measuring regional coordination. Focusing on Guangzhou as the case study area, this study constructs an urban–rural spatial accessibility assessment model integrating multimodal convolutional neural networks and Graph Neural Networks (GNN) to systematically examine the evolving accessibility patterns in last-mile logistics distribution across urban and rural spaces. The study finds that Guangzhou’s urban space continues to expand while rural space gradually decreases during this period, showing an overall development trend from centralized single-core to multi-polar networked patterns. The spatial accessibility of last-mile logistics in Guangzhou exhibits higher levels in urban core areas and lower levels in peripheral rural areas, but the overall accessibility is progressively expanding and improving in outlying regions. These accessibility changes not only reflect the optimization path of logistics infrastructure but also reveal the practical progress of urban–rural integration development. Through spatial distribution analysis and dynamic simulation of logistics networks, this study establishes a novel explanatory framework for understanding the spatial mechanisms of urban–rural integration. The findings provide decision-making support for optimizing last-mile logistics network layouts while offering both theoretical foundations and practical approaches for promoting co-construction and sharing of urban–rural infrastructure and achieving integrated regional spatial governance.