Implementation parameters.

In the face of increasingly diverse demands from tourists, traditional methods for scenic route planning often struggle to meet these varied needs. To address this challenge and enhance the overall service quality of tourist destinations, as well as to better understand individualized preferences of visitors, this study proposes a novel approach to scenic route planning and itinerary customization based on multi-layered mixed hypernetwork optimization. Firstly, an adaptive multi-route feature extraction method is introduced to capture personalized demands of tourists. Subsequently, a personalized tourist inference method based on a multi-layered mixed network is presented, utilizing the extracted personalized features to infer the true intentions of the tourists. Lastly, we propose a hypernetwork optimized route planning method, incorporating the inference results and personalized features to tailor the optimal touring paths for visitors. The results of our experiments underscore the efficacy of our methodology, attaining an accuracy score of 0.877 and an mAP score of 0.881 and outperforming strong competitors and facilitating the design of optimal paths for tourists.面对游客日益多元的出行需求，传统景区路线规划方法往往难以适配此类多样化诉求。为应对上述挑战、提升旅游目的地整体服务品质，并更精准地把握游客的个性化偏好，本研究提出一种基于多层混合超网络优化的新型景区路线规划与行程定制方案。首先，本文提出自适应多路线特征提取方法，用于捕捉游客的个性化出行需求。随后，本文提出基于多层混合网络的个性化游客意图推理方法，借助提取的个性化特征推断游客的真实出行意图。最后，本文提出超网络优化的路线规划方法，结合推理结果与个性化特征为游客定制最优游览路径。实验结果证实了所提方法的有效性：其准确率得分达0.877，平均精度均值（mean Average Precision，mAP）得分达0.881，优于各类强基准对比方法，可有效辅助游客规划最优游览路径。