School of Economics and Finance, Xi'an Jiaotong University, Xi'an 710000, China.
School of Animal Science and Technology, Northwest A and F University, Xianyang 712100, China.
Comput Intell Neurosci. 2022 Aug 3;2022:3029528. doi: 10.1155/2022/3029528. eCollection 2022.
The purpose is to find out the problems existing in the consumption economy structure of the scenic spots and to promote the rationalization of the consumption economy of the scenic spots. Based on the analysis of the applicability of the backpropagation neural network (BPNN) model, it uses BPNN to analyze the economic development level of Overseas Chinese Town East (OCT East). Firstly, the weight of each index is determined by the Analytic Hierarchy Process (AHP), and the expected value of the comprehensive evaluation is obtained. Secondly, to ensure the validity of the evaluation model for the development level of the tourism complex, the BPNN model is trained and tested to enable it to be applied to the evaluation of the economic development level of OCT East. The development level of OCT East from 2012 to 2021 is divided into three stages: high, higher, and lower. The development characteristics and existing problems of the OCT East are analyzed, and the optimization strategy of the consumption economy of the scenic spots is put forward in a targeted manner. The research results manifest, that from 2012 to 2021, the development level index of OCT East increased from 0.2457 to 0.5304, and it was in a state of steady growth. In 2019, the development level index reached 0.6497, and it was upgraded to "high-level," but the average development level index of OCT East was only 0.5662, and there was a lot of room for improvement. According to the divided evaluation indicators, the development level of OCT East is evaluated. In 2012, the development level was low. From 2013 to 2018, it was at a high level, and from 2019 to 2021, it was a high level of development. By studying the Tourism Consumption Structure (TCS) of scenic spots in the OCT East, the research method of the consumption economic structure has been expanded. Therefore, it not only provides a reference for optimizing the consumption of scenic spots, but also contributes to the progress of the social tourism economy.
目的是找出景区消费经济结构中存在的问题，促进景区消费经济合理化。基于对反向传播神经网络（BPNN）模型适用性的分析，采用 BPNN 对东部华侨城（OCT 东部）的经济发展水平进行分析。首先，利用层次分析法（AHP）确定各指标权重，得到综合评价的期望价值。其次，为保证旅游综合体发展水平评价模型的有效性，对 BPNN 模型进行训练和测试，使其能够应用于 OCT 东部经济发展水平的评价。将 OCT 东部 2012 年至 2021 年的发展水平划分为高、较高和低三个阶段。分析 OCT 东部的发展特点和存在的问题，提出有针对性的景区消费经济优化策略。研究结果表明，2012 年至 2021 年，OCT 东部发展水平指数从 0.2457 增长到 0.5304，呈稳步增长态势。2019 年，发展水平指数达到 0.6497，升级为“高”级，但 OCT 东部的平均发展水平指数仅为 0.5662，仍有很大的提升空间。根据划分的评价指标对 OCT 东部的发展水平进行评价。2012 年发展水平较低。2013 年至 2018 年为高水平，2019 年至 2021 年为高水平发展。通过对东部华侨城景区旅游消费结构（TCS）的研究，拓展了景区消费经济结构的研究方法。因此，不仅为优化景区消费提供了参考，也为社会旅游经济的发展做出了贡献。
