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Abrasive belt grinding, as the last process of blade shape machining, directly affects the final shape accuracy of blades and then affects their working efficiency and service performance. Material removal in abrasive belt grinding is a multi-factor coupling nonlinear process that makes accurate prediction of material removal a great challenge. The authors previously adopted the grinding spark and sound for online identification of material removal rate (MRR), achieving a high-precision online identification by effectively avoiding multiple factors and their coupling effects. This paper mainly studies the prediction method of material removal depth contour (MRDC) based on material removal rate, so as to realize the online evaluation of grinding results. From a geometric point of view, the unit material removal volume is calculated as the cross-section area multiplied by the grinding length per unit of time. The irregular cross-section area enclosed by the workpiece surface before grinding and MRDC after grinding is calculated by integration. The pressure distribution model is used to calculate the distribution coefficient through the known MRR. The experimental results show that the prediction accuracy of the proposed model can reach 93%, and it has good robustness for grinding parameters and abrasive belt wear status, which provides theoretical support for evaluating grinding results online. This is an earlier research work on the intelligent grinding process for dimensional accuracy improvement. [ABSTRACT FROM AUTHOR]