Quantitative Study on the Construction and Application Effectiveness of Graffiti Wall Painting Teaching Models in Public Space Contexts

Graffiti and mural painting, as essential forms of public visual art, require not only aesthetic planning but also high-precision implementation and control. This study constructs a structured teaching model based on a four-phase framework—"theme design—wall planning—process control—outcome evaluation"—integrated with computational support. Key techniques include image vector mapping, RGB-based color deviation analysis, and 3D wall surface modeling through point cloud reconstruction. Teaching effectiveness was verified via data collected from public wall implementations, using quantitative metrics computed through computer vision modules and spatial analysis algorithms. Results demonstrate significant improvements: 93.8% task completion rate, 16.5% gain in compositional consistency, 21.7% reduction in boundary color error, and a 0.76-point increase in public acceptance score (p<0.05), confirming the feasibility and technical reliability of this computationally augmented art education mode.This study demonstrates that integrating graffiti mural instruction into a structured teaching framework and employing quantitative assessment methods enhances the controllability and replicability of public art education, providing methodological insights for public space art education practices.