Personalized product search enhances user experiences by tailoring search results to individual preferences based on search logs. Traditional methods emphasize extracting features to build interest profiles but often fail to account for the dynamic variations in user attention to different product attributes (e.g., brand, category). These approaches typically combine all attribute features, relying on models to discern useful patterns from complex scenarios, which limits their effectiveness in capturing nuanced user preferences. To address this gap, we propose a Dynamic Multi-Attribute Interest Learning Model that captures the influence of individual attributes on user interests. Our model introduces two distinct profiling modules: attribute-centered profiling, which identifies preferences for specific attributes, and attribute-aware profiling, which explores multi-attribute correlations within the user’s search history. Additionally, we design a dynamic contribution weights strategy to explicitly guide the model in evaluating the impact of various attributes on user preferences. Experimental evaluations on large-scale datasets demonstrate the superiority of our approach, achieving significant improvements in search accuracy and user satisfaction compared to existing methods. This work advances the understanding of personalized product search by effectively modeling dynamic user interests and attribute interactions.