Indices for the diagnosis of hyperacute cerebral infarction (HACI) and the prediction of prognosis are essential for timely and appropriate management. MicroRNAs (miRNAs) that regulate gene expression following stroke have potential use as prognostic markers of HACI. Here, we explored whether concentrations of circulating miRNAs correlate with clinical outcomes and thus form a system of stroke stratification. Plasma samples from patients with HACI (n = 7) and age-matched healthy volunteers (HVT, n = 4) were screened by microarray to find differentially expressed miRNAs, some of which were further verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) (HACI:HVT = 33:23). The target genes of the miRNAs with verified differential expression were investigated by GO and KEEG analyses. Using the TOAST (OCSP) criteria and the 3-month modified Rankin Score (mRS), relationships among the expression patterns of specific miRNAs, stroke stratification, and clinical prognosis were determined. The microarray analysis revealed 12 differentially expressed miRNAs. Among seven selected miRNAs verified with qRT-PCR, miR-16 expression in the HACI group was the most significantly different from the HVT group (P < 0.01). Bioinformatics analysis showed that the potential target genes of miR-16 were mainly involved in programmed cell death and the p53 signaling pathways. Receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of miR-16 was 0.775 (sensitivity 69.7% and specificity 87%) and 0.952 (sensitivity 100% and specificity 91.3%) in overall patients and patients with large artery atherosclerosis (LAAS), respectively. Elevated miR-16 expression was associated with the stroke subtype of LAAS, total anterior circulation infarction, partial anterior circulation infarction, and poor prognosis (P < 0.05). A diagnostic method based on rapid measurement of plasma miR-16 has the potential to identify hyperacute cerebral infarction with LAAS with high sensitivity and specificity, which would inform and improve early treatment decisions and disease management.