Objective To develop a nomogram to predict surgical site infection (SSI) after spinal surgery, so as to help medical staff to plan preventive strategies to reduce the incidence of SSI. Methods We retrospectively selected 2 348 patients undergoing spinal surgery at Wenzhou People's Hospital from May 2012 to May 2019. Univariate and multivariate logistic regression models were used to determine independent predictors for SSI after spinal surgery and a nomogram was constructed to predict SSI after spinal surgery based on each predictive factor. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminatory ability of the nomogram and the consistency of the nomogram was tested using calibration plot and Hosmer-Lemeshow  goodness of fit test (H-L test). Results In univariate and multivariate analysis, current smoking (OR=2.242, 95%CI:1.125-4.469), diabetes (OR=0.932, 95%CI:1.260-5.113), operation time≥180 min (R=7.256, 95%CI:3.537-14.886), ASA ≥ grade III (OR=2.963, 95% CI:1.454-6.037) and blood transfusion (autologous blood OR=3.685, 95%CI:1.203-11.284; allogeneic blood OR=6.443, 95%CI:2.765-15.017) were identified as significant independent predictors. The nomogram was developed using these independent predictors. The AUC of the multivariate model for discrimination was 0.818 (95%CI:0.802-0.863). The calibration plot and H-L test (χ2=12.337, P=0.195) showed good consistency between predicted and observed outcomes. Conclusion The nomogram based on smoking, diabetes, operation time, ASA classification and blood transfusion has potential as a clinically useful predictive tool of SSI after spinal surgery.