The peripherally inserted central catheter (PICC) technology is widely used in medium and long-term venous treatment, but it can cause various complications and adverse reactions, such as PICC-related thrombosis. The continuous development of machine learning and deep neural networks provides a solution for the assisted diagnosis of PICC-related thrombosis based on clinical medical data. In this study, a fusion model of DeepFM and XGBoost is constructed to predict the risks of PICC-related thrombosis, which can perform feature fusion for sparse data and reduce over-fitting. The experiment reveals that the fusion model can effectively extract the feature importance of PICC-related thrombosis, predict the probability of disease, assist the clinic in identifying high-risk factors of thrombosis in PICC, and intervene to prevent the occurrence of thrombosis in time.