The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were ( 27.8 ± 30.6 ) μm and ( 29.1 ± 30.9 ) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was ( − 6.31 ± 2.82 ) μm, and that in group B was ( − 6.45 ± 3.06 ) μm. The 95% confidence interval of the difference between the two is −7.66~−5.43 μm. In patients with severe dry eye, the average CCT was ( − 3.78 ± 1.13 ) μm in group A and ( − 7.09 ± 2.05 ) μm in group B ( P < 0.05 ). The 95% confidence interval of the difference between the two is −7.05~ −5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe ( P < 0.05 ). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome.