ArticleScientific reports2025
Research on intelligent decision support platform for tourism enterprises based on multi-source heterogeneous data fusion.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
To address the critical challenges of decision-making delays and accuracy deficiencies faced by tourism enterprises in dynamic environments, this study develops an intelligent decision support platform based on multi-source heterogeneous data fusion. The research proposes a novel hybrid data fusion algorithm that integrates weighted averaging, Bayesian inference, and Dempster-Shafer evidence theory, establishing a comprehensive framework for processing three types of heterogeneous data from diverse sources. The main theoretical contributions include: (1) a hybrid data fusion algorithm achieving superior accuracy through adaptive weight adjustment mechanisms, (2) a multi-dimensional environmental adaptability assessment framework tailored for tourism enterprises, and (3) real-time decision optimization models with self-learning capabilities. Empirical validation across three different types of tourism enterprises demonstrates significant improvements in decision accuracy (18.96% average increase), response time reduction (78.4%), and environmental adaptability enhancement across market responsiveness, risk prediction, and resource optimization dimensions. The results indicate substantial cost-benefit ratios (3.4:1) and sustained competitive advantages, validating the platform's effectiveness for tourism enterprise digital transformation and strategic decision-making in volatile business environments.
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