Purpose: Nomograms, graphical representations integrating independent prognostic factors, are valuable tools in colorectal cancer research. Bayesian models for variable selection in survival outcome prediction offer advantages through Bayesian model averaging (BMA). This study aimed to utilise BMA for variable selection and develop a clinician-friendly online dynamic nomogram for survival prediction. Methods: A retrospective study used the Cabrini Monash colorectal neoplasia database, including patients who underwent surgery for colon cancer. Data on demographics, perioperative risks, treatment details, mortality, morbidity, and survival were collected. BMA was employed for Bayesian variable selection to identify effective risk factors for survival prediction. Sensitivity analyses using Cox-LASSO and imputation of missing data were performed. Prognostic online dynamic nomograms were constructed using selected risk factors and the R-package DynNom. Results: The study included 2,475 colon cancer patients from February 2010 to December 2021, with an overall mortality rate of 6.4 per 100 population (95% CI: 5.9-7.1), a relapse-free mortality rate of 7.0 per 100 population (95% CI: 6.4-7.6), and 5-year overall survival and relapse-free survival probabilities of 0.75 (0.72-0.77) and 0.74 (0.72-0.76), respectively. The dynamic nomogram integrated selected risk factors (e.g. ASA, stage, LNR, differentiation), providing a clinician-friendly online tool for clinical prognosis prediction. Conclusion: The developed clinician-friendly online dynamic nomogram offers clinicians valuable insights into the impact of these variables on survival outcomes, contributing to improved prognostic accuracy and personalised healthcare practices for colon cancer patients.