Accurately predicting nitrogen (N) digestion and utilization will allow diet optimization to achieve improved N efficiency. The objectives of this study were to revise the representation of urea recycling in Molly cow model and evaluate the revisions. The work included 1) modification of the existing urinary urea excretion equation to include BW as a scalar; 2) supplement of urea gut entry rate to derive parameters related to urea return to rumen; and 3) reparameterization of equations related to urea N recycling and ruminal N metabolism. Model parameters were changed from initial values to optimized values. Model predictions were compared with a data set from 12 published studies with 54 treatment means before and after the revisions. Mean squared errors were assessed to characterize model performance. Residual analyses demonstrated that the modifications improved the accuracy of predictions of ruminal N digestion, absorption and recycling. After the modifications, prediction errors for duodenal flows of total N, microbial N, non-ammonia N, and non-ammonia non-microbial N were 14.8, 22.4, 17.8 and 28.2%, respectively, which were all approximately 2% units less than observed with the initial model due primarily to the decreased mean bias. Compared with the initial model, predictions of ruminal ammonia and blood urea concentrations were greatly improved with substantial decreases in mean and slope bias. Prediction errors for gut entry rate were 19.2% with 0.93% mean bias and 1.73% slope bias, which indicated that urea N recycling mechanisms were properly represented in the model. Although the accuracy of urinary urea flow was improved, it still had 81.7% prediction error, which implies high variation of urinary urea N secretion may exist in collected studies, and therefore the reparameterization is not necessarily more accurate even though it fits the observed data. In summary, the model modifications led to a robust representation of urea N recycling and ruminal N metabolism which enabled more accurate and precise predictions of the effects of feeding and management decisions on N efficiency, thus contributing to sustainability of the dairy industry.