To address the issues of low accuracy, poor reliability, and difficulties in programmatic implementation associated with traditional methods for identifying outliers in dam deformation, using the Jinping Ⅰ Dam as a case study, a nonlinear statistical model for dam deformation was developed based on trend surface analysis. Using the generated three-dimensional trend surface as a benchmark, an outlier identification envelope was constructed by setting allowable errors, and a program implementation pathway was designed at the computer system level. In response to the causes and data characteristics of outliers, a comprehensive solution for determining the nature of outliers was proposed, integrating remote system recall testing, self-diagnosis of data acquisition equipment status, and Euclidean distance criteria between initial and repeated measurements. The results demonstrate that the proposed method achieves highly accurate dynamic identification of deformation outliers without requiring dynamic computation. The generated three-dimensional trend surface possesses clear physical and mechanical significance, enabling precise evaluation of dam deformation behavior under varying reservoir water levels and ambient temperatures.