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 graphization, 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 MB model size, with peak memory of 7.4 GB and inference latency of 10.1 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.