In modern operating systems, CPU scheduling policy selection and evaluation still rely mainly on heuristic methods, especially at the single-processor level or the abstract ready-queue level, and there is still a lack of systematic modeling and interpretable analysis for complex workload patterns. Traditional approaches are easy to implement and respond quickly in specific scenarios, but they often fail to remain stable under dynamic workloads and high-dimensional features, which can harm generalization. In this work, we build a simulation dataset that covers five typical scheduling policies, redesign a deep learning framework for scheduling policy identification, and propose the MCFCTransformer-DD model. The model extends the standard Transformer with multiscale convolution, frequency-domain augmentation, and cross-attention to capture both low-frequency and high-frequency signals, learn local and global patterns, and model multivariate dependencies. We also introduce a Dendrite Network, or DD, into scheduling policy identification and decision support for the first time, and its gated dendritic structure provides a more transparent nonlinear decision boundary that reduces the black-box nature of deep models and helps mitigate overfitting. Experiments show that MCFCTransformer-DD achieves 94.50% accuracy, a 94.65% F1 score, and an AUROC of 1.00, which indicates strong policy identification performance and strong potential for decision support.

Major depressive disorder (MDD) is a common disabling psychiatric disorder mainly diagnosed through clinical interviews and rating-scale assessments. Magnetic resonance imaging (MRI) provides a promising basis for computer-aided MDD identification, particularly through multimodal models that can integrate structural and functional brain information. However, existing multimodal MRI fusion models often use data-level fusion or post hoc feature concatenation, potentially overlooking differences in spatial resolution, voxel organization, and region of interest (ROI)-level semantic correspondence. To address this issue, this study proposes MUSA, a unified ROI semantic space model for multimodal MRI-based MDD classification. An Automated Anatomical Labeling (AAL) atlas-guided ROI semantic space establishes consistent brain-region correspondence across heterogeneous MRI features. An ROI-level 3D Vision Transformer (3D ViT) encodes ROI voxel information into fixed-dimensional representations, allowing structural, local functional, and connectivity information to be organized and integrated within a consistent brain-region semantic space. Experiments on 2380 participants from REST-meta-MDD validated MUSA for MDD and normal controls (NCs) classification. The feature combination of white matter (WM), functional connectivity (FC), regional homogeneity (ReHo), and degree centrality (DC) achieved 79.20% ± 0.65% accuracy and an AUC of 87.08% ± 0.51%. Brain-region importance analysis identified classifier-derived discriminative patterns across frontoparietal, sensorimotor, visual, temporal, and limbic-related regions.