Virtual Museum Experience Design and User Behavior Analysis

In the evolving landscape of digital cultural heritage, the integration of advanced computational models into immersive virtual environments has become essential for enriching user engagement and fostering more meaningful interpretative experiences. As digital heritage platforms increasingly adopt interactive technologies, there is a growing need to move beyond static presentations and introduce intelligent, responsive systems that adapt to individual users’ interests and behaviors. Traditional virtual museum systems often suffer from rigid content structures, limited adaptability, and a lack of personalized storytelling, all of which constrain user immersion and diminish the potential for impactful learning experiences. To overcome these challenges, our approach integrates semantic-topological modeling with adaptive interaction mechanisms, forming a dynamic and personalized digital museum experience. The proposed framework features a hybrid computational architecture that unites symbolic knowledge representations with algorithmic spatial layout generation. This integration enables the creation of flexible narrative trajectories that are contextually relevant and customized according to each user’s cognitive and behavioral profile. Furthermore, the system incorporates reinforcement-driven semantic planning to interpret and respond to user interactions in real time, ensuring a continuously evolving and coherent interpretative journey. Experimental results reveal substantial improvements in user satisfaction, engagement levels, and knowledge retention when compared to traditional virtual museum platforms. These outcomes highlight the scalability and transformative potential of the model, reinforcing its capacity to enhance accessibility, personalization, and educational value across diverse digital heritage applications.