Intelligent Pattern Recognition and Semantic Segmentation Techniques for Large-Scale Automation in Digital Projects

This paper introduces the Multi-Level Semantic Echo Network (MSEN), a unified framework for intelligent pattern recognition and semantic segmentation in large-scale digital projects. MSEN integrates cross-modal graphitization, semantic echo projection, hierarchical adaptive modulation, and multi-task co-decoding to capture both local precision and global semantic coherence. Comprehensive experiments on COCO, Cityscapes, and ADE20K benchmarks demonstrate its superiority over state-of-the-art baselines. On Cityscapes, MSEN achieves 82.7% mIoU and 97.1% pixel accuracy, surpassing HRNet and SegFormer, while on COCO and ADE20K it attains 46.6% and 44.0% mIoU, respectively, consistently outperforming competing methods. Efficiency analysis further reveals that MSEN requires only 168[Formula: see text]MB model size, with peak memory of 7.4 GB and inference latency of 10.1[Formula: see text]ms on 512 2 GPU input, making it lightweight and energy-efficient. These results confirm that MSEN delivers high accuracy, strong generalization, and practical efficiency, advancing large-scale automation in digital transformation scenarios.