Autism spectrum disorder （ASD） stands as one of the most prevalent and genetically inherited neurodevelopmental disorders， characterized by a multitude of clinical symptoms， notably featuring social communication deficits. Effective identification of biomarkers holds paramount significance in facilitating early interventions for ASD. Many current methods leverage multi-site imaging data to augment sample size， thereby enhancing diagnostic accuracy. However， the heterogeneity of data across multiple sites， resulting from variations in imaging devices， imaging parameters， and data processing workflows， is frequently overlooked. To overcome the above problem， this paper proposes a graph structure learning method for multi-site autism diagnosis based on multi-view low-rank subspace （MVLL-GSL）. Firstly， the multiple views of brain network are constructed for each sample， encompassing diverse topological information. Subsequently， samples from different classes are projected into their respective low-rank subspaces to mitigate the impact of data heterogeneity. Finally， the integration of graph structure learning with multi-task graph embedding learning， incorporating prior subnetworks and multi-view consistency regularization constraints， aims to extract more discriminative and coherent features from multi-view low-rank subspaces. The autism public ABIDE （Autism brain imaging data exchange） database is used to verify the proposed method. Experimental results show that the MVLL-GSL method improves the performance of ASD disgnosis and explains the association of different prior sub-networks with ASD pathogenesis.