In recent years， functional brain networks have been used in the diagnosis of brain disorders such as autism spectrum disorder （ASD）. Existing studies have shown that combining resting-state functional magnetic resonance imaging （rs-fMRI） data as well as non-imaging information to form a population graph， and then learning and classifying the data by using graph neural network （GNN） is very effective in the diagnosis of ASD. However， most studies still face two challenges： First， the construction of functional connectivity matrices using methods such as Pearson correlation coefficient cannot effectively identify and analyze localized brain regions and biomarkers associated with diseases； second， it is difficult to efficiently learn multi-scale information about node features in population graphs on GNN. To solve these problems， a multi-scale residual fusion graph convolutional networks （MSRF-GCN） based on the attention mechanism is proposed. The algorithm efficiently localizes and identifies brain regions useful for diagnosis by designing a functional connection generator to extract temporally relevant features with remote dependencies. Meanwhile， the multi-scale information in the population graph is learned by designing a multi-scale residual fusion algorithm. The Edge Sparse strategy is also introduced to increase the sparsity of node connections by randomly discarding edges in the initial population graph， which in turn reduces the risk of overfitting during training. The effectiveness of MSRF-GCN in the diagnosis of ASD is demonstrated by the results of experiments performed on the autism brain imaging data exchange （ABIDE） program.