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<p>With the continuous expansion and change of the tourism market, the massive amount, complexity and dynamic change of tourism data make the establishment of tourism management decision support system has become an important issue in the tourism industry. In this paper, we use the plain Bayesian classification algorithm, the improved Apriori association algorithm, and the gray GM(1, N) prediction model to mine and process the tourism big data and combine the 3S technology and the Agent-based knowledge representation technology to realize the construction of the tourism management decision support system based on big data analysis, and make the optimal decision for the planning of tourist attractions and routes. The attraction classification method’s accuracy rate is 72.98%, and its feasibility is high. The integrated error of the adopted GM(1,7) model is only 2.587%, which is smaller than the 3.483% of the GM(1,1) model and the 4.594% of the linear regression model, and the model accuracy is high. The average response time and average TPS of the system are 8.70s and 15.89s, respectively, which generally meet the demand for the system’s processing capability. This study provides a reference for the construction of a decision support system for tourism management.</p>
</abstract>

With the continuous expansion and change of the tourism market, the massive amount, complexity and dynamic change of tourism data make the establishment of tourism management decision support system has become an important issue in the tourism industry. In this paper, we use the plain Bayesian classification algorithm, the improved Apriori association algorithm, and the gray GM(1, N) prediction model to mine and process the tourism big data and combine the 3S technology and the Agent-based knowledge representation technology to realize the construction of the tourism management decision support system based on big data analysis, and make the optimal decision for the planning of tourist attractions and routes. The attraction classification method’s accuracy rate is 72.98%, and its feasibility is high. The integrated error of the adopted GM(1,7) model is only 2.587%, which is smaller than the 3.483% of the GM(1,1) model and the 4.594% of the linear regression model, and the model accuracy is high. The average response time and average TPS of the system are 8.70s and 15.89s, respectively, which generally meet the demand for the system’s processing capability. This study provides a reference for the construction of a decision support system for tourism management.

Research and Development of Decision Support System for Tourism Management Based on Big Data Analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the continuous expansion and change of the tourism market, the massive amount, complexity and dynamic change of tourism data make the establishment of...

Research and Development of Decision Support System for Tourism Management Based on Big Data Analysis

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 20 mars 2024

Accepté: 03 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1606

Mots clés
Plain Bayesian classification, Apriori algorithm, Gray GM(1,N) prediction model, Tourism management decision support system, Big data analysis

© 2024 Yanling Xiao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research and Development of Decision Support System for Tourism Management Based on Big Data Analysis

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 20 mars 2024

Accepté: 03 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1606

Mots clésPlain Bayesian classification, Apriori algorithm, Gray GM(1,N) prediction model, Tourism management decision support system, Big data analysis

© 2024 Yanling Xiao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Plain Bayesian classification, Apriori algorithm, Gray GM(1,N) prediction model, Tourism management decision support system, Big data analysis