Objective: To analyze the correlative factors of invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess, and to construct and verify the online nomographic prediction model. Methods: A case control study. The clinical data of 213 diabetic patients with Klebsiella pneumoniae liver abscess admitted to the Third Affiliated Hospital of Soochow University from January 1, 2015 to December 31, 2021 were retrospectively analyzed. The patients were divided into the training set (149 cases) and the test set (64 cases) by stratified random sampling method at a ratio of 7∶3. Synthetic minority over-sampling technique(SMOTE) was used to process the imbalanced data, then Lasso regression was used to screen out the optimal feature variables in the training set and multivariate logistic regression model was used to construct the prediction model of invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess, and verify it in the training set and test set. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the prediction efficiency of the model, and the simple and online interactive dynamic web page column graph was constructed. Results: Among the 213 patients, 60 were males and 153 were females, aged of (61.4±12.0) years. A total of 25(11.74%) diabetic patients with Klebsiella pneumoniae liver abscess developed invasion syndrome, which were included in divided into invasive K.pneumoniae liver abscesses syndrome (IKPLAS) group, and the other 188 cases were in without invasive K.pneumoniae liver abscesses syndrome (NIKPLAS) group. SMOTE algorithm was used for oversampling processing, so that the ratio of positive and negative samples was 1∶1. In the oversampling training set, 5 main risk factors were screened based on Lasso regression, namely fasting blood glucose (λ=0.063), hemoglobin (λ=-0.042), blood urea nitrogen (λ=-0.050), abscess size (λ=-0.025) and sequential organ failure assessment (SOFA) score (λ=0.450), respectively. Multivariate logistic regression model showed that fasting blood glucose (OR=1.20, 95%CI: 0.98-1.48, P=0.006), hemoglobin (OR=0.90, 95%CI: 0.86-0.95, P<0.001), blood urea nitrogen (OR=1.22, 95%CI: 1.03-1.43, P=0.017), abscess diameter (OR=0.76, 95%CI: 0.61-0.94, P=0.010), SOFA score (OR=3.08, 95%CI: 2.18-4.36, P<0.001) were associated with invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess. The area under the curve of ROC in the training set was 0.966 (95%CI: 0.943-0.989), the sensitivity was 90.5%, and the specificity was 91.3%. The area under the curve of the validation set ROC was 0.946 (95%CI: 0.902-0.991), with a sensitivity of 79.6% and a specificity of 88.9%. The calibration curves drawn in the training set and the test set fit well with the ideal curve. DCA showed that the neomorph prediction model had a good clinical net benefit when predicting the risk of IKPLAS in patients with diabetes complicated with Klebsiella pneumoniae liver abscess was 0.10-0.40. Conclusions: Fasting blood glucose, hemoglobin, urea nitrogen, abscess size and SOFA score are the related factors for invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess. The constructed column graph can effectively predict the risk of invasion syndrome in patients with diabetes complicated with Klebsiae pneumoniae liver abscess.