Objective To analyze the risk factors for early mortality in patients with stage Ⅳ colorectal cancer, and further construct and validate Nomogram prediction model for early mortality in stage Ⅳ colorectal cancer. Methods A retrospective analysis was conducted on the clinical and pathological data of stage Ⅳ colorectal cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database in the United States from 2018 to 2020. The study data was randomly divided into a training cohort and a validation cohort at a ratio of 8∶2. Multivariate logistic regression analysis was performed in the training cohort to screen for risk factors for early mortality in stage Ⅳ colorectal cancer patients, and Nomogram prediction model was further constructed. Receiver operating characteristic curve (ROC), calibration curve, and clinical decision curve analysis (DCA) were plotted. Results Age (50–70 group, OR=1.984, P=0.007; >70 group, OR=1.997, P=0.008), marital status (OR=1.342, P=0.025), primary tumor differentiation of G3+G4 (OR=1.817, P<0.001), T4 stage (OR=1.434, P=0.009), N2 stage (OR=1.621, P<0.001), M1c stage (OR=1.439, P=0.036), no chemotherapy (OR=21.820, P<0.001), bone metastasis (OR=2.000, P=0.042), brain metastasis (OR=6.715, P=0.001) and liver metastasis (OR=1.886, P<0.001) were risk factors for all-cause early death in stage Ⅳ colorectal cancer patients. Age（50–70 group, OR=2.025, P=0.008; >70 group, OR=1.925, P=0.017), primary tumor differentiation grade of G3+G4 (OR=1.818, P<0.001), T4 stage (OR=1.424, P=0.013), N2 stage (OR=1.637, P<0.001), M1c stage (OR=1.541, P=0.016), no chemotherapy (OR=21.832, P<0.001), brain metastasis (OR=6.089, P=0.001), liver metastasis (OR=2.100, P<0.001) were factors for cancer-specific early death of stages Ⅳ colorectal cancer patients. Based on these variables, we constructed two Nomogram prediction models for all-cause early death and cancer-specific early death in stage Ⅳ colorectal cancer patients. The area under curve (AUC) value of the all-cause early death prediction model in the training queue was 0.874 [95% CI (0.855, 0.893)], and the AUC value of the cancer specific early death prediction model was 0.874 [95%CI (0.855, 0.894)]; the AUC value of the all-cause early death prediction model in the validation queue was 0.868 [95%CI (0.829, 0.907)], and the AUC value of the cancer specific early death prediction model was 0.867 [95%CI (0.827, 0.907)], indicating that the model had good predictive ability. The calibration curve showed that the predictive models had good consistency with the actual results for predicting early mortality in stage Ⅳ colorectal cancer, and the DCA curve showed that the models could provide patients with higher clinical benefits. Conclusion The predictive models established in this study have good predictive performance for early mortality in stage Ⅳ colorectal cancer patients, which is helpful for clinical physicians to identify high-risk patients in the early stage and develop personalized treatment plans in clinical practice.