The lack of monitoring data can bring difficulties to the dam's healthy monitoring , therefore, we need to present a scientific and rational interpolation method for interpolating missing data in order to gain complete and reliable monitoring data. A data interpolation method integrating Kernel Independent Component Analysis (KICA) with Relevance Vector Machine (RVM) for interpolating missing data of dam was proposed according to the spatial correlations of the measured datapoints. The KICA-RVM transformed the original independent variables nonlinearly by KICA and extracted the independent components to form the characteristic independent variable. The number of characteristic independent variables was determined by the Eigenvalue spectrum analysis and the disturbance of redundant information was eliminated. RVM was applied for regression modelling of the sampling data transofermed by using KICA, thus to obtain a model with strong nonlinear expression ability and good performance. Through analysis of the dam monitoring data of a project, the results showed that the interpolation method had a good precision and statability.