Reservoir dams, as critical infrastructures for flood control, hydropower generation, and agricultural irrigation, pose significant risks if breached, making regular safety monitoring essential. However, monitoring data often contains anomalies due to environmental factors, system malfunctions, and abnormal behaviors of the monitored objects. Detecting these anomalies is vital for effective data analysis and early risk identification to ensure the dam safety. Existing anomaly detection methods typically focus on gross outliers and overlook subtle, gradual anomalies. This paper proposes a gradual anomaly detection method based on Deep Support Vector Data Description (DSVDD). The method constructs multi-dimensional monitoring parameters as samples and trains an autoencoder using DSVDD to map the input data to a compact hypersphere. The anomaly score is derived from the distance of the input sample from the center of the hypersphere. The proposed method is validated using laser alignment data from a high core-wall rockfill dam, and the results demonstrate its superior performance compared to other methods.