Objective To explore the predictive value of combined model of coagulation function and inflammatory markers for chronic heart failure (CHF) complicated with deep venous thrombosis (DVT). Methods A total of 100 patients with CHF admitted to Luoyang Hospital of Dongzhimen Hospital, Beijing University of Chinese Medicine from February 2020 to October 2024 were enrolled and divided into thrombus group (n＝32) and non-thrombus group (n＝68) according to the presence or absence of DVT. The general data and laboratory indicators of the two groups were compared. The independent risk factors were screened by multivariate analysis and the Logistic prediction model was constructed. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of the model. Results Multivariate analysis showed that age, D-dimer(D-D), fibrinogen(FIB), C-reactive protein(CRP) and procalcitonin(PCT) elevated were independent risk factors for patients with DVT (all P＜0.05). The area under the curve (AUC) of this model for predicting patients with DVT was 0.965 (95% CI: 0.932～ 0.999). The calibration curve and Hosmer-Lemeshow test showed that the predicted probability of the model was in good agreement with the actual observed value, with an average absolute error of 0.020 and a high degree of calibration (χ2＝4.208, P＝0.616). Conclusion The combined model based on coagulation function and inflammatory markers can effectively predict the risk of DVT in patients with CHF.