Time series anomaly detection is an important research topic in the field of time series analysis. Due to complex spatio-temporal dependencies and randomness of the multivariate time series in real industrial scenarios， many existing anomaly detection methods for single dependency modeling cannot learn data features effectively. In addition， ignoring frequency domain information will lead to incomplete model feature representation. To address the above problems， a time series anomaly detection model based on frequency domain enhanced graph variational learning network — FeGvL （Frequency-domain enhancement Graph-variational Learning） was proposed. Firstly， after the block operation， the dependency in time dimension was modeled by self-attention. Secondly， the graph relationship features after frequency domain enhancement were mapped to the latent space. Finally， the graph aggregation attention network was used to extract features between entities， and the temporal dependency was combined to achieve generalized variational reconstruction. Experimental results on public datasets PSM （Pooled Server Metrics）， SWaT （Secure Water Treatment） and WADI （WAter DIstribution） show that the F1 value of FeGvL is higher than those of seven advanced anomaly detection methods such as GDN （Graph Deviation Network）， TranAD （Transformer-based Anomaly Detection）， and GReLeN （Graph relational Learning Network）， and the average F1 value of FeGvL is 1.7 percentage points higher than that of the second-best model GReLeN. It can be seen that the proposed method can capture spatio-temporal dependencies effectively， provide representation capabilities， and has high anomaly detection accuracy.