To address the limitations in autism diagnosis, such as insufficient multi-scale feature extraction and the inaccuracy of functional connectivity estimation using Pearson correlation, this study proposes a novel diagnostic framework based on the Multi-Scaled Self-Attention Graph Convolution Network (MS-SAGCN). The framework begins by applying Morlet wavelet transform and dynamic time warping to extract the time-frequency information of Blood-Oxygen-Level-Dependent (BOLD) signals and their multi-scale functional connectivity. A pre-trained embedding model is then used to enhance time-frequency features, which are combined with functional connectivity to construct multi-scale brain networks. Finally, MS-SAGCN is employed to integrate and enhance the data for the automatic diagnosis of autism. Experiments were conducted using the ABIDE dataset, and the results show that MS-SAGCN can effectively enhance the multi-scale brain network. The overall framework achieved an accuracy of 95.1%, a true positive rate of 97.4%, and an F1 score of 94.9% in the classification task, significantly outperforming other diagnostic models, demonstrating the promising application prospects of this model.