Multi-scale residual network-driven regional cultural color feature extraction and intelligent cultivation pathway for rural art design talent

The preservation of regional cultural color heritage faces mounting challenges from urbanization and aesthetic homogenization, while rural art design education lacks effective integration of intelligent technologies. This paper proposes a unified framework addressing both computational color analysis and talent cultivation for rural cultural revitalization. On the technical side, we design a multi-scale residual network (MSResNet) that employs parallel dilated convolutional branches with channel attention-weighted fusion to capture hierarchical chromatic features from regional cultural artifacts. A cross-modal semantic association mechanism further embeds extracted color features into a culturally annotated latent space, enabling context-aware interpretation beyond purely statistical description. On the educational side, we construct a three-stage intelligent cultivation pathway—comprising cognitive tutoring, database-driven practice, and personalized competency assessment—that operationalizes the model’s outputs within a pedagogically grounded framework. Experiments on the purpose-built RCC-6 K dataset demonstrate that MSResNet achieves a macro F1 score of 0.912 and a cultural association index of 0.831, outperforming representative baselines. A 16-week quasi-experimental pedagogical study with 86 vocational college students shows that the intelligent pathway yields a composite competency gain of 29.9%, significantly exceeding the 14.6% achieved under traditional instruction. These results confirm that structurally integrating multi-scale deep learning with semantic cultural grounding can advance both the computational characterization and educational transmission of regional color heritage.