ON Intelligent Interactive Model of Tourism Planning Multi Neuron Algorithm

Xiong Jiang, Yu Zhang


The research on establishing intelligent interactive system of tourism planning based on multi neuron neural network
model has practical signifi cance. 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.


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

Full Text:


Included Database


[1] 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.

[2] TajanpureRupalirupalidixit@gmail.comMuddanaAkkalakshmiamuddana @gitam. eduGITAM University,Hyderabad,Telangana,India. Circular

convolution-based feature extraction algorithm for classifi cation of high-dimensional datasets[J]. Journal of Intelligent Systems, 2021, 30(1):1026-1039.

[3] Zhao L , Wang W , Zhang W . SEM-Based Research on Infl uence Factors of Energy Conservation in Operation and Maintenance of Construction

Project[J]. Intelligent automation and soft computing, 2019, 25(4):705-713.

[4] MajorosTam√°smajoros. tamas@gmail.comUjv √°riBal√° zsujvarib@gmail.comIntelligent Embedded Systems Research Laboratory,Faculty of

Informatics,University of Debrecen,Debrecen,HungaryDepartment of Experimental Physics, Faculty of Science and Technology,University of

Debrecen,Debrecen,Hungary. Stability study of the neural network at particle physics detectors[J]. Carpathian Journal of Electronic and Computer

Engineering, 2018, 11(1):48-52.

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

[6] Tong M , Duan H , Luo X . Research on short-term traffi c fl ow prediction based on the tensor decomposition algorithm[J]. Journal of Intelligent and

Fuzzy Systems, 2020, 40(2):1-11.

[7] Zheng Y , Fan W , Han M . Research on multi-agent collaborative hunting algorithm based on game theory and Q-learning for a single escaper[J].

Journal of Intelligent and Fuzzy Systems, 2020, 40(3):1-15.

[8] Mengyuan H , D Qiaolin, Shutao Z , et al. Research of Circuit Breaker Intelligent Fault Diagnosis Method Based on Double Clustering[J]. Ieice

Electronics Express, 2017, 14(17):20170463-20170463.

DOI: http://dx.doi.org/10.18686/esta.v10i2.391


  • There are currently no refbacks.

Copyright (c) 2023 Xiong Jiang,Yu Zhang