Identification of Obstacles and Optimization Pathways for Sustainable Tourism in Southern Xinjiang: A Deep Learning Approach Based on GRU Sentiment Analysis

With the rapid expansion of the tourism industry in Xinjiang, which received a record 328 million tourists in 2025, identifying development bottlenecks is crucial for regional sustainability. This study aims to identify the core obstacles hindering sustainable tourism in Southern Xinjiang—the region’s fastest-growing sector—and proposes evidence-based optimization pathways. Utilizing a deep learning approach, we deployed a Gated Recurrent Unit (GRU) sentiment analysis model to parse 5800 online reviews from 38 representative A-level scenic spots. The analysis identified 28 distinct obstacle clusters across three categories: landscape, cultural, and comprehensive destinations. The results reveal significant site-specific differentiation: natural landscape sites like Bayanbulak are primarily constrained by environmental risks and safety hazards, while high-traffic cultural sites like the Ancient City of Kashgar face acute challenges from over-commercialization and cultural erosion. Based on these findings, this study introduces a macro-level diagnostic tool and proposes targeted optimization strategies within the ESG (Environmental, Social, and Governance) framework. These insights offer actionable references for policymakers to enhance tourism resilience and achieve high-quality sustainable development in sensitive frontier regions.