Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.