Application of Internet big data analysis technology based on deep learning in tourism marketing performance evaluation

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, this paper has completed the Internet marketing performance evaluation system for the tourism market by introducing big data analysis technology and in-depth learning technology. This system can effectively evaluate the actual development of the tourism industry, and the system design comprehensively considers the 360-degree 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. According to the principle of maximum membership, the following experimental results can be obtained: due to the poor performance evaluation results of a scenic spot, its membership is the largest. It can be seen from this that the Internet marketing performance evaluation system for the tourism market has practical application value and can effectively analyze 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.