Intelligent Interactive Model of Tourism Planning Based on Multi Neuron Algorithm

Xiong Jiang, Yu Zhang

Abstract


As a strategic and systematic development and management activity, tourism planning plays an important role in tourism, and its development degree is directly related to the economic and social benefits of the whole country. With the continuous improvement of China's national economic level and the great changes in people's quality of life and cultural concepts. How to use modern information technology to realize the effective integration of tourist information has become one of the problems to be solved. Therefore, the research on establishing intelligent interactive system of tourism planning based on multi neuron neural network model has practical significance. Firstly, this paper introduces the meaning, function and characteristics of intelligent interaction in tourism planning, and then studies the application of multi neuron algorithm. Based on this algorithm, an intelligent interaction model in tourism planning is designed, and its function is experimentally analyzed. Finally, the experimental results show that the basic tourism cost is controlled within 30000. According to the rationalization assumption of the mathematical model, through a reasonable combination of the days spent in each region, in order to visit the areas of interest to tourists in fewer days. The mathematical model of tourism route planning established in this paper can help travelers make reasonable travel plans, and can also judge whether the determined travel plans are reasonable and the approximate budget.


Keywords


Multi Neuron Algorithm; Tourism Planning; Intelligent Interaction; Interaction Model

Full Text:

PDF

Included Database


References


Li X, Liu D, Huang H, et al. Research on large data intelligent search engine based on multilayer perceptive botnet algorithm[J]. Journal of Intelligent and Fuzzy Systems, 2019, 37(2):1-10.

Tajanpure R, Muddana A. "Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets" Journal of Intelligent Systems, vol. 30, no. 1, 2021, pp. 1026-1039.

Zhao L, Wang W, Zhang W. SEM-Based Research on Influence Factors of Energy Conservation in Operation and Maintenance of Construction Project[J]. Intelligent automation and soft computing, 2019, 25(4):705-713.

Majoros,T. & Ujvári,B.(2018).Stability study of the neural network at particle physics detectors. Carpathian Journal of Electronic and Computer Engineering, 11(1) 48-52.

Bai X. Research on the customer churn model of e-commerce based on the improved combined intelligent algorithm[J]. Ce Ca, 2017, 42(4):1460-1464.

Tong M, Duan H, Luo X. Research on short-term traffic flow prediction based on the tensor decomposition algorithm[J]. Journal of Intelligent and Fuzzy Systems, 2020, 40(2):1-11.




DOI: https://doi.org/10.18686/esta.v9i4.294

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Xiong Jiang

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.