Artificial intelligence (AI) technologies are rapidly being applied across all segments of colorectal cancer management, including cancer screening, endoscopic diagnosis and treatment, imaging and pathological analysis, therapeutic decision‐making, intraoperative assistance, and follow‐up management. Nevertheless, substantial disparities exist among various AI systems in terms of task definition, data sources, validation methodologies, performance metrics, safety thresholds, and real‐world clinical practicability. There is an urgent need to establish a dedicated evaluation framework tailored to clinical scenarios specific to colorectal cancer. To address this demand, the Colorectal Surgery Group of the Chinese Society of Surgery (Chinese Medical Association), the Colorectal Cancer Committee of China Anti‐Cancer Association, and the Colorectal Surgeon Expert Group of the Surgeon Branch (Chinese Medical Doctor Association) jointly assembled a working group composed of specialists covering colorectal surgery, digestive endoscopy, medical imaging, pathology, medical oncology, radiation oncology, artificial intelligence, medical statistics, medical informatization, medical ethics, and regulations. Through systematic literature retrieval, collation of existing clinical guidelines and regulatory documents, expert letter consultations, and panel discussions, the working group formulated 13 recommendations graded by levels of evidence and strength of recommendation, and developed the Expert Consensus on Evaluation Standards for Artificial Intelligence Applications in Colorectal Cancer (2026 Edition). This consensus establishes a tiered evaluation framework centered on seven core dimensions: algorithmic performance, clinical safety, clinical efficacy, user experience, model interpretability, ethical compliance, and continuous supervision. It further clarifies targeted evaluation requirements for key AI application scenarios, including AI‐assisted lesion detection during endoscopy, AI‐aided diagnosis via imaging and pathological slides, generative AI and clinical decision support, intraoperative AI assistance, and real‐world surveillance of AI systems. The consensus underscores that AI systems shall only serve as auxiliary clinical tools and must not replace clinicians to independently render diagnostic, therapeutic or surgical decisions. Prior to clinical deployment, all AI systems for colorectal cancer shall undergo rigorous validation commensurate with their risk classification, implement disease‐specific safety red lines, mandate mandatory clinician review, and enforce full‐lifecycle supervision throughout clinical use.