Scientific evaluation of traditional village activation value constitutes the cognitive foundation and practical prerequisite for implementing hierarchical and categorical activation strategies. While current academic research has accumulated vast amounts of spatial morphology and cultural heritage data through field investigations, yielding rich regional case studies, existing classification methods predominantly rely on structural descriptions and subjective empirical judgments, lacking a universal large-sample classification system with national applicability. In the context of the digital technology revolution characterized by ubiquitous connectivity, traditional research paradigms dependent on manual surveying, image interpretation, and expert experience now face systemic challenges in processing multisource heterogeneous data. This study establishes a three-tier analytical framework of “data collection-model training-intelligent evaluation” based on deep learning technology, aiming to transcend the spatiotemporal limitations inherent in traditional village value assessment. The research systematically examines the technical adaptability of deep learning in village digital conservation, subsequently constructing a dual-dimensional identification system encompassing intrinsic characteristics and extensional potentials of traditional village activation value. Through feature extraction networks, it achieves quantitative analysis of villages’ inherent attributes and developmental capacities. The paper ultimately proposes three technical pathways for intelligent cognition of activation value: (1) multimodal data fusion methodology based on transfer learning; (2) dynamic evaluation model oriented towards activation value; (3) visualization support system for hierarchical decision-making.