Research on intelligent decision support platform for tourism enterprises based on multi-source heterogeneous data...

Area of Science:

• Business Intelligence
• Data Science
• Tourism Management

Background:

• Tourism enterprises face decision-making delays and accuracy issues in dynamic markets.
• Effective decision support is crucial for navigating market volatility and maintaining competitiveness.

Purpose of the Study:

• To develop an intelligent decision support platform for tourism enterprises using multi-source heterogeneous data fusion.
• To enhance decision accuracy, reduce response times, and improve environmental adaptability in the tourism sector.

Main Methods:

• A novel hybrid data fusion algorithm integrating weighted averaging, Bayesian inference, and Dempster-Shafer evidence theory.
• Development of a multi-dimensional environmental adaptability assessment framework.
• Implementation of real-time decision optimization models with self-learning capabilities.

Main Results:

• Significant improvements in decision accuracy (18.96% average increase) and response time reduction (78.4%).
• Enhanced environmental adaptability across market responsiveness, risk prediction, and resource optimization.
• Demonstrated substantial cost-benefit ratios (3.4:1) and sustained competitive advantages.

Conclusions:

• The intelligent decision support platform effectively addresses decision-making challenges in dynamic tourism environments.
• The platform facilitates digital transformation and strategic decision-making for tourism enterprises.
• The hybrid data fusion approach offers superior accuracy and adaptability for complex data integration.