Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term service.Current analysis methods used to obtain safety monitoring data are suited only to single survey point data.Unreliable or even paradoxical results are inevitably obtained when processing large amounts of monitoring data,thereby causing difficulty in acquiring precise conclusions.Therefore,we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high dams.In addition,we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high dams.Data metrics reduction is achieved using information fusion at the data level.A Bayesian information fusion is then conducted at the decision level to obtain a comprehensive diagnosis.Early-warning outcomes can be released after sorting analysis results from multi-positions in the dam according to importance.A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey points.It can likewise obtain precise real-time results and export comprehensive early-warning outcomes from multi-positions of high dams. Analyzing the service behavior of high dams and establishing early-warning systems for them have become increasingly important in ensuring their long-term service. Current analysis methods used to obtain safety monitoring data are suited only to single survey point data. UN sure or even paradoxical results are inevitably obtained when processing large amounts of monitoring data, thereby causing difficulty in obtaining precise precise.Therefore, we have developed a new method based on multi-source information fusion for conducting a comprehensive analysis of prototype monitoring data of high dams. In addition, we propose the use of decision information entropy analysis for building a diagnosis and early-warning system for the long-term service of high dams. Data metrics reduction is achieved using information fusion at the data level. A Bayesian information fusion is then conducting at the decision level to obtain a comprehensive diagnosis. Early-warning scores can be released after sort ing analysis results from multi-positions in the dam according to importance. A case study indicates that the new method can effectively handle large amounts of monitoring data from numerous survey points. It can likewise obtain obtain accurate real-time results and export comprehensive early-warning outcomes from multi-positions of high dams.