Sustainable tourist route design is a critical challenge in industrial heritage planning. While prior tourism routing algorithms predominantly minimize physical distance, and conventional heritage planning focuses on the static preservation of abandoned sites, both lack the multi-objective adaptability required for “living” industrial landscapes. In such dynamic environments, active production, tourism, and ecological conservation intricately coexist. To address this gap, this study proposes a novel, data-driven route planning framework, taking the Tianjin Changlu Salt Field as a case study. The genuine novelty lies in integrating multi-objective network optimization with spatial design implementation. The site is abstracted into a topological network comprising 13 nodes and 19 edges. Multi-attribute edge weights—incorporating spatial distance, travel time, landscape attractiveness, and ecological sensitivity—are quantified using entropy weighting fused with subjective preferences. Using the Floyd–Warshall algorithm, three theme-based touring routes are generated. Unlike traditional methods, this workflow actively translates algorithmic outputs into concrete spatial strategies, such as bypassing ecologically sensitive zones and transforming production facilities into perceptible landscape nodes. Comparative evaluations demonstrate that these optimized routes achieve higher comprehensive utility than baseline and designer-generated schemes, offering a pioneering, reproducible paradigm for the sustainable renewal of living industrial heritage.