Polycystic ovary syndrome (PCOS) is now a common gynecological endocrine disease, also known as Stein–Leventhal syndrome. Studies have found that Vaspin gene polymorphism is significantly associated with diabetes and cardiovascular disease, and PCOS has a clear glucose metabolism abnormality. So far, because the cause of PCOS is not clear, many problems such as the etiology, diagnostic criteria, prevention, and treatment of PCOS remain unsolved. Which also makes PCOS attract the attention of academic circles. Therefore, it is urgent to clarify the pathogenesis of PCOS, in order to explore the clinical correlation between the polymorphism of the Vaspin gene and polycystic ovary syndrome. This article introduces the correlation analysis study of Vaspin gene polymorphisms and polycystic ovary syndrome based on intelligent medicine. This article first selected 40 patients with PCOS as the experimental group and then selected 40 patients without PCOS as the control group. Secondly, through the detection methods of hs-CRP level detection and oil red O fat staining and passed two sets of control experiments. Finally, intelligent medical data analysis was used to analyze the location of the Vaspin gene in the experimental group and the control group. The final result showed that the correlation reached 75%.

Uterine adhesions are mainly manifested as menstrual changes in women of childbearing age and affect fertility. Resection of uterine adhesions can well restore the shape of the patient’s uterine cavity and improve the patient’s menstruation. However, how to promote the growth of the endometrium and prevent the recurrence of adhesions after the operation is still a major problem. This article aims to study the use of traditional treatment methods as a control and use low-frequency nerve therapy device to assist in the treatment of posterior intrauterine adhesions recurrence rate, menstrual recovery effective rate, adverse reaction rate, liver function, etc. to study the low-frequency nerve therapy device auxiliary treatment method to prevent the postoperative effect of intrauterine adhesions. This article proposes an image processing algorithm based on intelligent medical related algorithms such as deep learning, Apriori algorithm, and an improved algorithm that introduces the degree of interest and details of 140 patients diagnosed with moderate to severe intrauterine adhesions who underwent hysteroscopic TCRA surgery in a certain affiliated hospital. The medical records were followed up by hysteroscopy and electric resection, and they were randomly divided into a control group and an observation group, with 70 cases in each group. Both groups of patients were closely monitored postoperatively, followed by postoperative review, and recorded menstrual recovery and pregnancy. At the same time, we performed hysteroscopy for recurrence of endometrial adhesions. The experimental results of this article show that the actual treatment rate of the control group is 65.7%, which is much lower than the 95.7% of the experimental group. The probability of returning to normal after 3 months of menstruation in the control group was 34.0%, much lower than the 69% in the experimental group. Three months after operation, the endometrial thickness of the experimental group was much higher than that of the control group, and the RI was lower than that of the control group. The difference was statistically significant ( P < 0.05 ). The clinical treatment results are satisfactory and worthy of clinical screening.