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Promoting the coordinated development of digitalization and greening has become an important pathway for firms to achieve high-quality growth. Using panel data for A-share listed firms in China’s Yangtze River Basin from 2010 to 2022, this study examines the effect of supply chain innovation policy on firms’ digital–green development. We measure the synergy between digitalization and greening using a composite system synergy approach and identify the policy effect through a quasi-natural experiment based on the supply chain innovation policy, combined with a synthetic difference-in-differences model. The results show that the policy significantly improves the coordinated development of firm digitalization and greening, and the findings remain robust across a series of tests. Mechanism analysis indicates that this effect operates through three channels: easing financing constraints, increasing supply chain diversification, and promoting industrial chain modernization. Moderating effect tests further show that supply chain efficiency, supply chain resilience, and entrepreneurship strengthen the policy’s positive effect on digital–green development. Heterogeneity analysis suggests that the policy effect varies systematically with firm size, market competitiveness, and information asymmetry. This study provides micro-level evidence on how supply chain innovation policy can promote firms’ digital–green transformation and offers useful implications for policies aimed at improving firm competitiveness and supporting sustainable development.

Recommending visually coherent and stylistically diverse sets of artworks is a challenging task in digital curation, interior design, and personalized visual content services. Unlike traditional recommendation problems that focus on individual item relevance, composite artwork recommendation requires selecting a group of items that together satisfy a user’s stylistic intent while maintaining aesthetic compatibility. In this paper, we introduce a novel cloud-enabled graph-based framework for style-aware artwork composite recommendation. We construct an artwork correlation graph that models both the stylistic descriptors of individual artworks and their empirical compatibility based on historical co-occurrence. By leveraging distributed computation in cloud environments, our framework efficiently handles large-scale artwork collections and accelerates graph search. Given a user-defined set of style tags, our method identifies a minimal and connected subset of artworks that collectively cover the desired styles and form a coherent set in the graph. We formalize this task as a constrained subgraph selection problem and propose an efficient graph search algorithm supported by cloud-based parallelization to solve it. Experimental results on real-world artwork datasets demonstrate that our approach significantly outperforms existing baselines in terms of style coverage, visual harmony, and recommendation compactness. This work offers a principled and scalable cloud-oriented solution for generating aesthetically balanced and contextually appropriate artwork combinations.