Abstract: Because the outliers are inevitable in dam automation monitoring data,and the magnitude,quantity,and distribution of outliers are uncertain. So the manual deletion of outliers has a large workload and strong subjectivity. This method has a wide range of applications because it does not require sample learning based on feature tags. First of all,the trend item is extracted after the automatic monitoring data is decomposed and reconstructed. Then the obvious outliers in the remaining item are discriminated and eliminated by using the isolated forest algorithm. Finally,the other outliers are performed according to the Pauta criterion. Through the example verification,the method can meet the practical application of the project which can effectively detect the outliers of the dam automatic monitoring data and the false positive rate is low.