In the anomaly detection tasks for multivariate time series， the correlations between variables are complex， and such correlation is difficult to be learned by traditional anomaly detection methods clearly. In addition， most models only consider the correlation between variables， with learning time dependencies insufficiently. Therefore， a time series anomaly detection method based on High-order Feature Aggregation （HFA） was proposed. Firstly， a variable relationship diagram was constructed through graph structure learning. Secondly， the traditional Graph ATtention network （GAT） was enhanced by taking full account of higher-order neighbor node correlations， thereby modeling complex inter-variable relationships more accurately. Finally， temporal dependencies of the series were captured fully through the integration of one-dimensional convolutions with self-attention mechanism. Experimental results on four public datasets demonstrate that compared with the suboptimal baseline model Anomaly Transformer， HFA has the F1 score increased by 1.34% on average； compared with the current mainstream baseline method TopoGDN （Topology Graph Deviation Network）， HFA has the F1 score increased by 9.05% on average. The results of ablation experiments further verify the effectiveness of each module in the model.