Objective: To systematically sort out research hotspots, evolutionary stages and emerging frontiers of artificial intelligence (AI) applied in predictive early warning nursing for postoperative complications of gastrointestinal tumors via bibliometric visualization, so as to provide theoretical references for constructing intelligent perioperative nursing systems. Methods: Literature was retrieved from Web of Science Core Collection (WoSCC) on July 12, 2026 with a combined subject-term retrieval formula. Initial 136 records were deduplicated and screened by two researchers. Eligible English original articles and reviews published before July 12, 2026 were retained; conference abstracts, editorials and studies irrelevant to nursing early warning were excluded, leaving 120 papers for analysis via CiteSpace 6.3.R1. Analyses included keyword co-occurrence, clustering, citation burst detection and timeline mapping. Results: Publications grew exponentially after 2021, with 8 papers released by July 2026. China contributed the most papers (60, 50.0%), followed by the US (12). The keyword network contained 230 nodes and 338 links, forming 10 reliable clusters (Q = 0.6501, S = 0.8821). Main hotspots cover machine learning-based complication prediction models and electronic health record-based individualized risk stratification. “Risk prediction” and “prevention” are the 2025-2026 frontiers, shifting research focus from passive treatment to proactive predictive nursing. Conclusion: The research field of AI-driven early warning nursing for gastrointestinal tumor postoperative complications has entered a rapid development phase. Chinese scholars dominate total publication volume, while international cross-regional collaborative nursing research needs further reinforcement. Future research should prioritize developing interpretable, real-time intelligent warning tools compatible with clinical nursing workflows, and carry out multi-center prospective nursing verification to accelerate the clinical translation of AI technology, ultimately optimizing perioperative nursing quality and postoperative recovery outcomes of gastrointestinal tumor patients.
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