Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems. However, in existing research, multivariate data are often influenced by various factors during the data collection process, resulting in temporal misalignment or displacement. Due to these factors, the node representations carry substantial noise, which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance. Accordingly, this study proposes a novel multivariate anomaly detection model grounded in graph structure learning. Firstly, a recommendation strategy is employed to identify strongly coupled variable pairs, which are then used to construct a recommendation-driven multivariate coupling network. Secondly, a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network, while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data. Finally, unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm. Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.