A Multi‐Dimensional Feature Fusion Framework With XGBoost for IIoT‐Driven Behavioral Analytics in Industrial Internet Systems

Industrial Internet of Things (IIoT) systems generate massive behavioral data, demanding efficient analytics frameworks for real‐time monitoring. This study proposes a multi‐dimensional feature fusion framework integrating XGBoost, tailored for IIoT‐driven behavioral pattern recognition. A four‐dimensional architecture is constructed to analyze critical attributes across contact degree, status, duration, and social relations, leveraging edge‐computed IIoT footprints (e.g., mobile signaling, network interaction data). The framework defines three behavioral modes and achieves 98.89% precision, 98.85% recall, and 98.85% F1‐score via XGBoost. Feature importance analysis identifies key indicators such as mobile number status and interaction frequency. This work demonstrates the potential of harmonizing AI with IIoT data fusion, providing a scalable solution for real‐time monitoring in Industrial Internet and future network architectures.