The disc cutter is the most frequently damaged and consumed component in the TBM boring process. Especially under large sections of soft and hard composite strata, the frequent changes in the cutter load cause accelerated wear of the cutter, affecting the construction efficiency and cost. Thus, rapid and precise perception of cutter wear contributes to optimizing cutter replacement and operational strategies. This study proposes an intelligent prediction framework for cutter wear, considering the spatial movement properties of TBM in composite strata. Firstly, the cutter movement distance was calculated by a geometric relationship, and then a standardized dataset was established by the tunneling data preprocessing. Subsequently, a cutter wear prediction model, DCSPN, is proposed based on the cutting properties. Taking a project in China as a case study, the proposed model showed a prediction relative error of more than 80% and an R2 of more than 0.85 in the testing set. The results show that the proposed framework improves the perception accuracy of disc cutter wear in composite strata.