Abstract
Marketing through the network is the basic marketing method in the information age. It has certain marketing value, and its position has gradually become stable. It has become the mainstream marketing method, especially praised by the tourism industry, which has injected fresh blood into this field. As the development of tourism market is dynamic, businesses will consider the needs of tourists when paying attention to its development process, so as to design performance evaluation criteria for tourism marketing. Under this research background, by integrating big data analysis technology and deep learning technology, this paper has successfully developed an Internet marketing performance evaluation system for the tourism market. This system can effectively evaluate the actual development of the tourism industry, and the system design comprehensively considers the 360° performance appraisal and balanced integral models. Based on the actual tourism data of a certain place, this study summarizes some additional evaluation indicators, thus improving the evaluation model and laying the foundation for the introduction of data and model construction process of AHP. Applying the principle of maximum membership, the experimental results demonstrate that a scenic spot with poor performance evaluation results has the highest membership. This highlights the practical application value of the Internet marketing performance evaluation system for the tourism market, which effectively analyzes market performance. This paper improves the performance evaluation system by introducing in-depth learning and big data analysis technology into the tourism market performance analysis.
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Su, X., Yin, W. Application of Internet big data analysis technology based on deep learning in tourism marketing performance evaluation. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08482-5
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DOI: https://doi.org/10.1007/s00500-023-08482-5