Abstract
The digital age of artificial intelligence marks the rapid development of tourism engineering and the gradual improvement of intelligent management theory. This study aims to solve the problems of low efficiency of dynamic relationship analysis and low data utilization in traditional intelligent management methods of tourism engineering. This work studies the dynamic optimization model of tourism engineering management theory based on the artificial intelligence data analysis model and designs the dynamic analysis model of tourism engineering management data based on the convolution neural network. The model can collect dynamic data information of tourism management from many aspects and can also be used to study and analyze human behavior patterns based on the convolutional neural network algorithm. According to the human behavior data analysis model and convolution neural network algorithm, this study formulates the real-time management data scheme of tourism engineering and better extracts the characteristic information of the dynamic data of tourism engineering management. The results show that the topology optimization model of tourism intelligent management based on the convolutional neural network achieves high feasibility, high data accuracy, and high response speed. It can improve the collaborative coupling relationship between management efficiency and dynamic data in tourism engineering management based on big data analysis technology. It realizes the effective combination of tourism management, digital management, and artificial intelligence algorithm.
Highlights
At present, the traditional “many to many” cluster point information collection is the main data information of tourism project management in China [1]
Since the beginning of the 21st century, the rapid development of artificial intelligence technology in China has led to the transformation of data analysis methods in tourism engineering management in China. e emergence of modern tourism engineering management analysis methods such as intelligent tourism management theory planning, efficient management data analysis, and coordinated utilization of software and hardware has contributed to the large-scale promotion of “intelligent management of industrial 4.0 tourism engineering” and provides opportunities [2]
The existing intelligent management system of tourism engineering provides a large number of data information extraction schemes, it is difficult to select a targeted topological representation scheme according to its management dynamic theoretical system and the law of human behavior in the process of dynamic management, so as to achieve the optimal effect of topological analysis [4]
Summary
The traditional “many to many” cluster point information collection is the main data information of tourism project management in China [1]. Compared with the data mining model established by the traditional intelligent management analysis model in tourism engineering, which takes the continuity of static management data as the main research object, the innovation of this study is that the discrete dynamic modeling technology and big data topology analysis strategy based on the convolution neural network algorithm are applied to the dynamic analysis model of industry 4.0 tourism engineering management It can make full use of a large number of dynamic management data, extract appropriate management data feature information, realize the integrity approach at the simulation level, and quantitatively describe the quantitative representative eigenvalues, similarity of multidimensional management analysis modes, and expected evaluation indicators of different tourism projects in the process of employee management scheme allocation with multitransformed neural network factors. It can efficiently carry out customized analysis on the factors affecting management efficiency and accuracy
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