Objective To analyze the influencing factors for surgical site infection (SSI) based on real-world data (RWD) from healthcare-associated infection (HAI) monitoring system. Methods From January 2019 to December 2021, inpatients of a tertiary first-class hospital in Nanchang were selected as the study subjects. Differences between the covariates of SSI group and the non-infected group were balanced by 1∶1 propensity score matching (PSM), and the influencing factors for SSI were analyzed by multivariate logistic regression. Results 24 507 subjects were contained in the study, out of which 210 cases had SSI (incidence 0.86%), including 141 cases (67.14%) of superficial incision tissue infection. A total of 94 pathogenic strains were isolated, including 64 (68.08%) Gram-positive bacteria, 28 (29.79%) Gram-negative bacteria and 2 (2.13%) fungi. 210 pairs were successfully matched with PSM. After matching, there was no statistically significant difference between two groups in terms of age, gender, year, department, and whether or not with emergency treatment (all P>0.05). Multivariate logistic regression analysis showed that risk factors for SSI were pre-operative hospital stay ≥6 days, operation duration ≥3 hours, clean-contaminated incision and contaminated incision, whereas endoscopic surgery and prophylactic use of antimicrobial agents were the protective factors. Conclusion PSM can reduce the selection bias of routine data collection research, balance group differences of baseline data, and help hospital to make full use of the big data of HAI management system to explore the management indicators of individualized control.