Abstract: OBJECTIVE To explore the risk factors for invasive fungal infection（IFI） in patients with solid tumors during inpatient chemotherapy, to establish a predictive model and analyze its predictive efficacy. METHODS Total of 301 solid tumor patients admitted in the Department of Oncology of Shaoxing City Keqiao District Hospital of Traditional Chinese Medicine General Hospital from Jan 2014 to Jan 2018 were recruited as the model group, and 285 solid tumor patients admitted from January 2018 to January 2021 were in the verification group. The clinical data was collected by using electronic medical records, and an IFI prediction model for solid tumor patients was established. The receiver operating characteristic curve（ROC） analysis model was used to analyze the diagnostic value of IFI between the model group and the control group. RESULTS Univariate analysis showed that the length of hospital stay, diabetes mellitus, history of fungal infection, preventive antifungal medication, and long-term glucocorticoid use were related to the incidence of IFI in solid tumor patients of the model group（P＜0.05）. but were related to gender, age, and tumor type, tumor staging, combined with hypertension, combined with chronic obstructive pulmonary disease（COPD）, combined with chronic kidney disease), history of stroke is irrelevant; Multivariate logistic regression analysis showed that the length of hospital stay＞14 d, combined with diabetes, history of fungal infections, no preventive use of antifungals and long-term use of glucocorticoid were independent risk factors for IFI in patients with solid tumors（P＜0.05）. ROC analysis showed that the areas under the diagnostic curve of the predictive model for IFI prediction were 0.931 and 0.907, with the SE of 0.026, 0.031. and the 95% Cis of 0.881-0.982, 0.846-0.967, all of which were significant（P＜0.001）. Hosmer-Lemeshow goodness-of-fit test showed that the difference between the model established in this study and the actual value was not significant（Hosmer-lemeshow χ~2=2.153, P=0.565）, indicating the effective value of the model in the prediction of IFI in patients with solid tumors in our hospital. CONCLUSION The solid tumor prediction model established based on clinical data is effective in the prediction of IFI, which can be used as a reference for the clinical prevention, management and treatment of IFI.