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Construct an automated visual style recognition and hybrid recommendation framework for environmental art design images across ambiguous style categories and diverse user preferences.

August 16, 2026Advanced ElectromagneticsOpen Access

Research on Automatic Recognition and Intelligent Recommendation System for Environmental Art Design Styles Based on Deep Learning

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Construct an automated visual style recognition and hybrid recommendation framework for environmental art design images across ambiguous style categories and diverse user preferences.
Built and preprocessed a dataset of 150,000 environmental art design images across 10 style categories annotated through expert voting.
Trained an improved ResNet-50 network featuring channel–spatial hybrid attention via stage-wise transfer learning to generate 256-dimensional style embeddings.
Constructed dynamic long- and short-term user preference vectors from behavioral logs and deployed a hybrid recommendation pipeline integrating style similarity, collaborative filtering, and diversity penalties.
The attention-enhanced ResNet-50 model achieved 92.7% Top-1 accuracy and 98.4% Top-5 accuracy, outperforming VGG-16, Inception-v3, and baseline ResNet-50 architectures.
The dynamic preference-fusion recommendation strategy attained a click-through rate of 28.1% and a diversity entropy of 1.82.

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Implication

Experimental study demonstrates 92.7% accuracy and improved recommendation diversity using an attention-enhanced ResNet-50, suggesting scalable personalized matching in visual design.

Cite This Study

W. Du (2026) studied this question.

synapsesocial.com/papers/6a817a72f2fb91fc834ae499 https://doi.org/10.7716/aem.v15i3.4027

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Implication

Experimental study demonstrates 92.7% accuracy and improved recommendation diversity using an attention-enhanced ResNet-50, suggesting scalable personalized matching in visual design.

Research on Automatic Recognition and Intelligent Recommendation System for Environmental Art Design Styles Based on Deep Learning

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Cite This Study

Research on Automatic Recognition and Intelligent Recommendation System for Environmental Art Design Styles Based on Deep Learning

Key Points

Discussion

Implication

Cite This Study

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