With the rapid advancement of the digital economy, digital copyright assets have become a pivotal indicator of competitiveness and innovation for publishing institutions. Traditional static evaluation approaches prove insufficient in capturing the multifaceted, dynamic nature of digital copyrights, especially in contexts where user interaction, social sharing, and data-driven feedback loops continuously reshape asset value. This study constructs a comprehensive evaluation framework based on the concept of data assetization, aiming to quantify the real-time, multi-dimensional value of digital copyrights. The proposed “four-dimensional dynamic coupling model” integrates content value, relational network value, data value, and derivative development value, capturing the synergistic effects across these dimensions. The model’s theoretical foundation rests on complex systems theory and industrial ecology, highlighting how non-linear interactions—such as user behavior, content quality, and derivative product potential—drive value emergence.To ensure accuracy and scientific rigor, this study implements a hybrid weighting approach combining the Analytic Hierarchy Process (AHP) and entropy-based methods, with dynamic weight adjustment mechanisms reflecting market trends and user activity indices. Data collection includes content quality metrics (e.g., citation counts, ratings), network value indicators (user scale, interaction frequency, and influencer contributions), behavioral data (both structured and unstructured), and derivative development factors (intellectual property (IP) adaptability and cross-media potential). The reliability and validity of the proposed model were verified through multiple tests, including Cronbach’s α (>0.85), confirmatory factor analysis (CFI > 0.93), and historical case back-testing of 15 prominent IPs (e.g., Joy of Life, Battle Through the Heavens), achieving an average deviation of 9.6% compared with actual transaction values. Empirical results indicate that content with the higher user engagement and data richness demonstrates significantly greater value amplification through social media interaction and derivative commercialization. Case studies, including Netflix’s recommendation system and the #BookTok phenomenon, illustrate how dynamic user feedback and algorithmic distribution mechanisms enhance copyright value, providing insights into the non-linear growth trajectory of digital assets. Furthermore, the model reveals that derivative development (e.g., spin-offs, merchandise, cross-media adaptations) not only increases revenue streams but also feeds back into content and network value, thereby establishing a self-reinforcing cycle of value co-creation.The findings provide actionable strategies for publishers and digital content owners, highlighting the integration of big data analytics, AI-driven recommendation systems, and blockchain-based copyright tracking to optimize value management. While the model demonstrates high adaptability and precision, challenges such as data governance, privacy protection, and cross-platform collaboration remain critical for its large-scale application. Future research directions include refining the model automation, enhancing real-time evaluation capabilities, and establishing unified industry standards for digital copyright valuation.In summary, this study advances both theoretical breakthrough by combining data assetization with multi-dimensional dynamic modeling and delivers a practical tool for strategic decision-making in the publishing industry. By aligning content production, user interaction, and technological innovation, the proposed framework facilitates the maximization of both economic and social value of digital copyrights, ultimately contributing to the sustainable evolution of the digital publishing ecosystem.