Year: 2025
Type: conference-paper
Abstract: This study designed and implemented an AI-based system for art style analysis and creative guidance, exploring the application of deep learning and multimodal learning in art creation. By employing convolutional neural networks (CNNs) and deep learning models, an efficient algorithm for style feature extraction and transfer was proposed. Combined with multi-dimensional annotated image datasets, this approach achieved precise style analysis. The system architecture includes personalized creation guidance and multimodal optimization modules, providing deep learning-based creation recommendations through user behavior and style analysis data. Test results show that the system performs excellently in style classification, style consistency, and personalized recommendations, maintaining stability even under high concurrency. This research provides innovative pathways for the application of AI in art creation, promoting the development of intelligent creation assistance systems.
Authors: Peng Fu, Qingnan Wang
Institution: Changchun Humanities and Sciences College
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413985585