The diagnosis of autism spectrum disorder （ASD） mainly relies on the patient’s medical history and clinical symptoms， and there is still a lack of objective evaluation indicators. Therefore， the discovery of disease-related biomarkers is essential for early identification and intervention. Although the multi-site brain imaging data have increased the sample size and improved the statistical power， which helps to improve the diagnostic performance of autism， the current research is often plagued by data heterogeneity. To address this issue， a discriminative domain adaption via low-rank representation （DDA-LRR） framework for multi-site ASD identification is proposed. Specifically， we first transfer both source and target data to a common subspace， where each source data can be represented by a combination of source samples such that the distribution differences can be well relieved. Then， we learn an orthogonal reconstruction matrix， which can preserve the main energy in the obtained low-dimensional embedding space and thus is appropriate for the subsequent learning tasks. Finally， to ensure the discriminative ability of the low-rank representation， we use the label information of the source data to integrate the classification loss into the training stage. An efficient optimization strategy based on the alternating direction method of multipliers method is developed to solve the proposed DDA-LRR method. Experimental results show that the proposed method can reduce the differences in data distributions of multiple sites， realize the effective transfer of knowledge， and improve the diagnosis performance of multi-site ASD effectively.