To address the problems that fuse trimming in chip manufacturing still relies on sequential search of trimming codes, leading to frequent read-write operations, long testing time, and high hardware resource consumption, this paper designs a single-shot trimming-code prediction model based on a GBDT multi-classification framework. Overfitting-resistant mechanisms are introduced at the structural level: during residual learning, part of the residual samples is randomly discarded to weaken excessive memorization of abnormal and noisy samples, while Gaussian perturbations are injected into the leaf-node outputs to enhance robustness and generalization under fluctuations in data distribution. In addition, a fuzzy-region discrimination strategy is incorporated to construct a mathematical description of trimming-code fuzzy regions, enabling secondary discrimination and correction of samples located near adjacent class boundaries, thereby effectively reducing misclassification between neighboring categories and achieving refined identification of uncertain trimming codes. Experimental results show that, in the fuse-trimming indexing task, the proposed model achieves an overall prediction accuracy above 97.8% and an R2 value greater than 0.99, while significantly shortening test time and reducing read-write operations and hardware resource overhead.