High-Quality Development Path for Tourism Economy: Tourist Behavior Mining and Destination Marketing Optimization Driven by Intelligent Analytics Technology
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Abstract
In the context of the digital economy, the sustainable development and quality growth of the tourism economy increasingly rely on data-driven decision-making, especially through the integration of multi-source sensing data, mobile communication records, online behavioral information, and destination service data. With the support of wireless communication infrastructure, location-aware services, and intelligent information systems, tourism destinations can more accurately perceive visitor flows, behavioral preferences, and service demands, thereby improving resource allocation and marketing responsiveness. This paper aims to build a systematic research framework by combining multi-source data and making full use of intelligent analytics technologies, including deep learning algorithms, natural language processing, and clustering algorithms, to comprehensively analyze the inherent patterns, emotional preferences, and decision-making mechanisms of tourists’ psychological activities. The paper focuses on how analytical results can be translated into effective destination marketing optimization strategies at the individual level, including personalized recommendations, dynamic pricing, visitor flow understanding, and tourism product innovation. These strategies are further used to promote effective destination governance, improve service quality, and support value co-creation for tourists. This paper establishes a data-driven analysis paradigm for tourism management and provides practical decision-support tools for high-quality economic growth. By combining methodological rigor with intelligent sensing, wireless data acquisition, and destination-level information service systems, this research offers managers actionable insights for enhancing the structural and experiential quality of the tourism economy.
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