Objective To explore the functional differences of hippocampal network in patients with different outcomes of acute disturbance of consciousness （aDOC） by functional magnetic resonance imaging （fMRI）， construct and verify the support vector machine （SVM） machine learning prediction model. Methods Clinical data of 43 patients with aDOC admitted to the Department of Neurosurgery of the First Affiliated Hospital of Nanjing Medical University and completed fMRI examination from September 2022 to July 2023 were retrospectively analyzed. All patients were followed up for three months after discharge. The revised version of the Coma Recovery Scale （CRS-R） was used to evaluate the consciousness of the subjects during follow-up， and patients with unqualified imaging data were excluded. Finally， 37 patients were included， including 19 patients with spontaneous cerebral hemorrhage and 18 patients with traumatic brain injury. According to the follow-up CRS-R score， patients with aDOC were divided into emergence from minimally conscious state （eMCS） group （n=13） and prolonged disorders of consciousness （pDOC） group （n=24）. The clinical data of two groups of patients were compared. Based on the MATLAB platform， fMRI data were analyzed for hippocampal functional network FC values. The internal SVM code in MATLAB was used for machine learning， while leave-one-out was used for cross validation. Receiver operating characteristic （ROC） curve was adopted to demonstrate predictive performance. Results There was no statistically significant difference in clinical and demographic data between the two groups of patients （P＞ 0.05）. There were statistically significant differences in Glasgow Coma Scale score ［（9.0±1.8） vs. （6.0±2.1）］ and Full Outline of Unresponsiveness Scale score ［13.00 （11.00， 13.00） vs. 10.00 （8.25， 11.75）］ between eMCS group and pDOC group （t=3.67， Z=-3.24； P＜ 0.01）. The comparative sequence hippocampal network analysis of blood oxygen level dependent （BOLD） in fMRI showed that there were statistically significant differences in brain activity between the two groups of patients in the bilateral anterior cingulate cortex （t=4.632， P＜0.005， TFCE corrected） and the right lingual gyrus （t=3.940， P＜ 0.005， TFCE corrected）. The ROC curve of the SVM model based on the differences in FC values of the hippocampal network in all brain regions using fMRI data showed that the area under the ROC curve （AUC） was 0.85， the sensitivity was 0.69， and the specificity was 0.83. The AUC， accuracy， sensitivity， and specificity of the SVM model based on the anterior cingulate cortex in different brain regions were 0.88， 81.08， 0.86， and 0.83， respectively. The AUC， accuracy， sensitivity， and specificity of the SVM model based on the right lingual gyrus in different brain regions were 0.75， 70.27， 0.77， and 0.71， respectively. Conclusions There are differences in the activity of fMRI hippocampal networks in the bilateral anterior cingulate cortex and right lingual gyrus of aDOC patients with different outcomes. Based on these differences in brain regions， the machine learning model can be constructed to accurately predict the outcome of aDOC patients， which provides ideas and targets for exploring the recovery mechanism and treatment of aDOC.