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Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder caused by both environmental and genetic factors. However, its etiology and pathogenesis remain unclear. The purpose of this study was to establish an immune-related diagnostic model for ASD using bioinformatics methods and to identify ASD biomarkers. Two ASD datasets, GSE18123 and GSE29691, were integrated into the gene expression Database to eliminate batch effects. 41 differentially expressed genes were identified by microarray data linear model (limma package). Based on the results of the immune infiltration analysis, we speculated that neutrophils, B cells naive, CD8+ T cells, and Tregs are potential core immune cells in ASD and participate in the occurrence of ASD. Finally, the differential genes and immune infiltration in ASD and non-ASD patients were compared, and the most relevant genes were selected to construct the first immune correlation prediction model of ASD. After the calculation, the model exhibited better accuracy. The calculations show that the model has good accuracy.