Abstract: In order to comprehensively leverage the substantial volume of data generated during the drilling process and facilitate intelligent analysis and decision support for real-time risk monitoring and alerting, a drilling intelligent decision support system has been developed. This system is based on a Client/Server (C/S) three-tier architecture and a data-centric platform, integrating fusion of physical models, intelligent algorithms, and trend analysis technologies. The system's functionality encompasses the acquisition, fusion, and management of heterogeneous data from multiple sources, achieved through the analysis of the origins, structure, and purpose of drilling data. Techniques such as data transmission, natural language extraction, and data fusion have been seamlessly integrated to accomplish this task. The system further incorporates a model fusion mechanism, taking into consideration the coupled effects of hydraulics and pipe mechanics during the drilling process, culminating in the establishment of a real-time digital wellbore system. Building upon this foundation, a risk anomaly detection algorithm has been devised by considering the trends in deviations between predicted and measured parameters. This algorithm integrates accident mitigation measures with an alert mechanism, thereby enabling the system to provide timely risk warnings and support decision-making in the face of unforeseen incidents. The application of this system spans over 50 wells across various types, including shale oil and gas horizontal wells and deep wells. The observed alignment between predicted warnings and on-site occurrences attains a commendable 91.5% conformity rate. Notably, the system has played a pivotal role in optimizing drilling parameters and enhancing risk monitoring, thereby substantiating its practicality and feasibility. Consequently, it serves as a robust safeguard for the realization of efficient and secure drilling operations.