Objective To analyze the independent risk factors for carbapenem-resistant Klebsiella pneumoniae （carbapenem-resistant Klebsiella pneumoniae，CRKP） infection in clinical patients， and further establish a nomogram model applicable for individualized risk prediction. Methods Clinical data of 422 patients with Klebsiella pneumoniae infection admitted to Chaohu Hospital Affiliated to Anhui Medical University from January 2023 to June 2025 were collected retrospectively. Using R software， the data were randomly split into a training set （295 cases） and a validation set （127 cases） at a ratio of 7∶3. LASSO and Logistic regression were used to identify risk factors， and a nomogram for the CRKP infection risk prediction model was further constructed with its performance analyzed. Results Multivariate analysis revealed five independent risk factors for CRKP infection in patients， namely ICU admission （OR = 4.883， 95%CI： 1.842 ~ 12.946）， tracheotomy/tracheal intubation （OR = 4.784， 95%CI： 1.763 ~ 12.981）， sputum aspiration （OR = 2.309， 95%CI： 0.767 ~ 6.954）， carbapenem administration （OR = 4.832， 95%CI： 1.944 ~ 12.009）， and combined antibacterial agent use （OR = 2.239， 95%CI： 0.938 ~ 5.341）. Subsequently， a prediction model was established based on these five variables screened from the training set， with the formula expressed as： Logit（P） = -3.632 + ［ICU admission （yes = 1， no = 0）］× 1.586 + ［Carbapenem use （yes = 1， no = 0）］× 1.575 + ［combined use of antibiotics （yes = 1， no = 0）］× 0.806 + ［sputum suction （yes = 1， no = 0）］× 0.837 + ［tracheotomy/tracheal intubation （yes = 1， n o =0）］× 1.565. The area under the receiver operating characteristic curve （AUC） was 0.921 （95% confidence interval ［CI］： 0.880~0.961） in the training set and 0.914 （95% CI： 0.846 ~ 0.983） in the validation set. The calibration curves generated for both the training set and validation set in this study demonstrated favorable consistency between the probability of CRKP infection predicted by the established model and the actual CRKP infection probability observed in clinical practice. The Hosmer-Lemeshow （H-L） test indicated that the model had a good degree of calibration. Decision curve analysis （DCA） further validated the favorable clinical utility of the constructed model. Conclusion The constructed risk prediction model can well predict high-risk patients with CRKP infection and can be used as a tool to assess the risk of CRKP infection in hospitalized patients.