Objective To construct and verify the diagnostic model of preoperative malignant risk of ovarian tumors, so as to improve the diagnostic efficiency of existing test indexes. Methods The related serological indicators and clinical data of patients with ovarian tumors confirmed by pathology who were treated in the Affiliated Hospital of Southwest Medical University between January 2019 and September 2023 were retrospectively collected, and the patients were randomly divided into a training set and a verification set at a 7∶3 ratio. Logistic regression was used to construct a diagnostic model in the training set, and the diagnostic efficacy of the model was verified through discrimination, calibration, clinical benefit, and clinical applicability evaluation. Results A total of 929 patients with ovarian tumors were included, including 318 cases of malignant ovarian tumors and 611 cases of benign ovarian tumors. The patients were randomly divided into a training set of 658 cases and a validation set of 271 cases. A diagnostic model was constructed using logistic regression in the training set, containing 5 factors namely age, percentage of neutrophil (NEU%), fibrinogen to albumin ratio (FAR), carbohydrate antigen 125 (CA125), and human epididymis protein 4 (HE4): modelUAM=−3.211+0.667×age+2.966×CA125+0.792×FAR+1.637×HE4+0.533×NEU%, with a Hosmer-Lemeshow test P-value of 0.21. The area under the receiver operating characteristic (ROC) curve measured in the training set was 0.927 [95% confidence interval (0.903, 0.951)], the sensitivity was 0.947, and the specificity was 0.780. The area under the ROC curve of the validation set was 0.888 [95% confidence interval (0.840, 0.930)], the sensitivity was 0.744, and the specificity was 0.901. Conclusion A new quantitative tool based on age, NEU%, FAR, CA125 and HE4 can be used for the clinical diagnosis of ovarian malignant tumors, and it is helpful to improve the diagnostic efficiency and is worth popularizing.