Research on the Optimization of Cultural Tourism Resource Allocation and Tourist Flow by Integrating Reinforcement Learning
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Abstract
Efficient allocation of cultural tourism resources and balanced tourist-flow distribution are essential for improving regional tourism sustainability and service quality. To address the dynamic and multi-objective characteristics of resource allocation and tourist guidance, this study proposes an intelligent optimization framework integrating deep reinforcement learning. A dynamic environment model incorporating resource attractiveness, capacity constraints, and tourist behavioral prediction is first established. An improved Deep Q-Network combined with a multi-agent collaborative mechanism is then employed to optimize resource supply, traffic regulation, and route planning simultaneously. Furthermore, a reward function considering tourist satisfaction, regional balance, and resource utilization is designed to support adaptive decision-making. Experimental results based on five major tourist cities indicate that resource utilization increases from 68.7% to 93.3%, peak tourist concentration decreases to 40.9%, and overall satisfaction improves to 0.84. The proposed framework provides an effective solution for intelligent tourism management and offers methodological references for dynamic decision optimization, information propagation analysis, and intelligent service networks.
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