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A wireless network-based machine intelligence model for green tourism satisfaction analysis

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Abstract

This paper proposes a wireless network-based machine intelligence model to enhance the predictive capability of green tourism satisfaction in regions with cultural differences. The model combines big data analysis techniques and machine learning algorithms to collect tourist data through intelligent machines and devices. Analyzing tourists’ behavior and preferences establishes a reliable prediction model to effectively assess tourists’ satisfaction with green tourism. Additionally, the paper constructs a distributed integration scheduling and feature mining model of green tourism satisfaction, which extracts relevant information features of big statistical data using an integration feature detection method. Furthermore, the paper establishes a fuzzy network structure model and designs the objective function of green tourism satisfaction prediction through decision scheduling and parameter optimization. The model’s parameter-weighted learning enhances the optimization prediction ability of green tourism satisfaction under regional cultural differences. The paper also carries out green tourism satisfaction prediction and tourists’ preference feature analysis using the fuzzy big data matching feature clustering method. The fuzzy network block fusion and clustering results also enable tourists’ preference behavior feature analysis, further improving the predictive capability of green tourism satisfaction under regional cultural differences. The simulation results demonstrate high accuracy and good optimization ability, ultimately improving green tourism satisfaction prediction.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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  1. Zibo Vocational Institute, 506 Liantong Road, Zhoucun District, Zibo, 255000, Shandong, China

    Xue Li

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Li, X. A wireless network-based machine intelligence model for green tourism satisfaction analysis. Wireless Netw 30, 1107–1120 (2024). https://doi.org/10.1007/s11276-023-03546-8

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