Abstract: In order to deeply leverage the large amount of data generated during the drilling process and achieve intelligent analysis and auxiliary decision-making for risk monitoring while drilling, a drilling intelligent decision support system has been developed. This system was developed based on a client/server (C/S) three-tier architecture and data platform, integrating with the physical models, intelligent algorithms, and trend analysis technologies. Through analyzing the source, structure, and usage of drilling data, combined with data transmission, natural language extraction, and data fusion technology, the data acquisition, fusion, and management of multi-source heterogeneous data have been achieved. Taking into account the coupling effect of hydraulics and tubular mechanics during the drilling process, the model fusion mechanism was designed, and the digital wellbore system while drilling was established. On this basis, combined with the deviation trend of predicted parameters and measured parameters, a risk anomaly monitoring algorithm was established, and thus the risk alerting and decision-making support could be realized through integrating construction measures with the alerting mechanism for downhole failure and complex situations. This system has been applied in over 50 exploration wells across various types, including shale oil and gas horizontal wells and deep wells, with an accuracy rate of 91.5% between predicted results and actual field results. Its practicality and feasibility was thereby verified. The drilling intelligent decision support system can realize drilling parameter optimization and risk monitoring, thereby it serves as a robust technical safeguard for the efficient drilling and safe operations.