Background Colorectal cancer ranks among the top three most prevalent cancers worldwide posing a significant challenge despite surgical intervention. Detecting tumor recurrence early is paramount to improving the survival outcomes of patients post-colorectal cancer surgery. Presently recurrence prediction relies heavily on clinicians' subjective judgment drawing from clinical examination blood tests imaging and other clinical data. However this method lacks objective reliability and risks patients missing optimal intervention opportunities. While new diagnostic methods have emerged they are often costly and challenging to implement universally. Moreover they typically operate reactively detecting tumors only after significant in vivo development serving as a 􀆴 debriefing􀆶 mode. Method This study is based on retrospective cohort data collected from patients with colorectal cancer admitted to the Department of Gastrointestinal Surgery and Emergency Surgery at Sichuan Provincial People 's Hospital Affiliated Hospital of University of Electronic Science and Technology of China between January 2013 and October 2018. Stringent exclusion criteria will be applied during patient selection and clinical data from colon cancer patients are gathered spanning five years before and after surgery. The collected data will undergo thorough cleaning preprocessing classification and enrichment processes to create an AI-ready dataset. Leveraging a combination of data-driven and mechanistic modeling approaches we aim to develop a digital twin model capable of monitoring and predicting colorectal cancer recurrence within a five-year timeframe. Conclusion Current studies have shown that the recurrence rate of colorectal cancer five years after surgery is as high as 30% and a reliable tool for early warning of postoperative recurrence of colorectal cancer is urgently needed in clinic. Digital twin technology can realize multi-dimensional data processing establish multi -module model prediction and establish a prediction model with time series which is suitable for the prediction of colorectal cancer patients' recurrence. Based on digital twin technology this study intends to establish a prediction model of tumor recurrence in patients with colorectal cancer within 5 years after surgery in order to achieve the monitoring and early warning of tumor recurrence and reduce the mortality of patients with colorectal cancer recurrence after surgery.