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Leveraging multiple data sources to enhance tourism resource management and visitor behavior analysis has become a key challenge in the context of the booming smart tourism industry. In this study, we explore how to integrate and optimize multiple data sources including social media activities, user reviews, tourism statistics, and geographic information to build a comprehensive information management platform for smart tourism resources. Given the limitations inherent in isolated and decentralized data processing approaches in the smart tourism domain, we propose a new approach using deep learning autoencoders for efficient extraction and fusion of meaningful features from heterogeneous datasets. Our methodology encompasses a rigorous data collection and preprocessing phase, ensuring data quality and consistency, followed by the application of autoencoders to learn high-level feature representations conducive to data integration. The fused data facilitate the development of strategies for the optimal allocation of tourism resources and nuanced analysis of visitor behavior patterns. Experimental evaluations demonstrate the model’s proficiency in capturing intricate data relationships, significantly enhancing the predictive accuracy for tourism demand forecasting, and enabling personalized visitor recommendations. The results underscore the potential of our approach to revolutionize smart tourism management practices by providing actionable insights into resource optimization and visitor engagement strategies, thereby contributing to the sustainable growth of the tourism sector.

In the era of digital transformation, leveraging multi-source sensor data to analyze and enhance student learning behavior has become increasingly crucial. In this research, we propose a Learn-Sync Intelligent Fusion (LSIF) system that delves into Estimate Adam driven- Intelligent Gradient Boosting Machines (EA-IGBM) that integrate sensor data from diverse sources to provide extensive analysis for the student behavior based on engagement and performance using multi-source sensor data fusion. This research employs the sensor data for online and offline classes for predicting the student learning behaviors during the class. To provide a holistic view of student engagement and performance, these sensor data are normalized using min–max normalization. Recursive Feature Elimination (RFE) is employed to extract the normalized multi-source data to frame multiple features that are integrated using the feature-level fusion technique. The LSIF aggregates behavioral fusion data from online and offline activities, using the EA-IGBM algorithm to predict academic success and provide actionable feedback. This model stimulates using tensor flow 2.15. The proposed EA-IGBM algorithm for both offline and online learning engagement detection performance is more significant, which utilizes certain parameters like student engagement, interaction frequency, behavioral patterns, and distractive levels. The established system highlights the effectiveness of multi-source sensor data fusion in monitoring and optimizing student learning behavior.