Artificial intelligence-driven ecological environment monitoring and sustainable development assessment technology for cultural and tourism construction zones

Location planners face a critical challenge in balancing environmental protection with economic development amid rapid growth in cultural and tourism zones. Traditional environmental monitoring approaches often rely on limited indicators and manual interpretation, which are insufficient to capture the complex spatial–temporal interactions between natural systems and human activities.To address this limitation, this study proposes an AI-Driven Integrated Ecological Sustainability Framework (AIESF) for real-time environmental monitoring and sustainability assessment. The proposed system integrates multi-source remote sensing data, IoT-based environmental sensors, and deep learning models for ecological analysis. A hybrid convolutional–temporal attention mechanism is employed to detect dynamic changes in pollution levels, land use patterns, and biodiversity-related indicators.The framework enables early identification of environmental degradation and supports data-driven decision-making for sustainable tourism management. Experimental results demonstrate that the proposed AIESF achieves an accuracy of 85.7%, an F1-score of 82.6%, an environmental sensitivity of 0.81, and an inference time of 3.0 s, with stable performance across evaluations.Overall, the proposed method provides a scalable and practical solution for continuous ecological monitoring and sustainable tourism management in culturally sensitive and tourism-intensive regions.