BACKGROUND Robotic pancreaticoduodenectomy (RPD) is a novel type of minimally invasive surgery to treat tumors located at the head of the pancreas. This study aimed to construct a novel prediction model for predicting selection preference for RPD in a Chinese single medical center population. MATERIAL AND METHODS The clinical data from 451 pancreatic ductal adenocarcinoma patients were collected and analyzed from January 2013 to December 2016. Twenty-three items affecting clinical strategies were optimized by LASSO (least absolute shrinkage and selection operator) regression analysis and then were incorporated in multivariable logistic regression analysis. C-index was used for evaluating the discriminative ability. Decision curve was applied to determine clinical application of this model and the calibration of this nomogram was evaluated by calibration plot. The model was internally validated through bootstrapping validation. RESULTS Clinicopathological factors included in the model were age, history of diabetes mellitus, history of hypertension, history of heart, brain and kidney disease, history of abdominal surgery, symptoms (jaundice, accidental discovery and weight loss), anemia, elevated carcinoembryonic antigen (CEA), smoking, alcohol intake, American Society of Anesthesiologists (ASA) scores, vascular invasion, overweight, preoperative lymph node metastasis and tumor size >3.5 cm. A C-index of 0.831 indicated good discrimination and calibration of this model. Interval validation generated an acceptable C-index of 0.787. When surgical approach was determined at the threshold of preference possibility less than 63%, decision curve analysis indicated that this model had good clinical application value in this range. CONCLUSIONS This new nomogram could be conveniently used to predict the selection preference of robotic surgery for patients with pancreatic head cancer.