In order to solve the problems of scattered relationships between elements in fashion design, the difficulty of structured expression of user preferences, and the lack of interpretation of recommendation results, this paper proposes a knowledge graph based method for the construction of clothing design element association network and intelligent recommendation. The design, color, material, process and scene semantics are integrated into the unified computing framework. Through multi-source data collection, label generation, relationship extraction, user preference clustering and scheme combination optimization, a complete technology chain is formed from knowledge organization, association reasoning to recommendation output. Based on 2680 clothing samples and 18642 user interaction records, the experiment constructs a clothing design knowledge graph, and uses the rule response space to depict the differences between user groups. The results show that the F1 value of the proposed method in the construction of the element association network reaches 0.857, the contour coefficient of user clustering is 0.64 when K=5, the average acceptance rate of the recommendation scheme is 0.794, and the rule overlap rate and output consistency in the cross-task test remain above 0.79. The results show that the knowledge graph can effectively enhance the integrity of the relationship expression of clothing design elements, the stability and interpretability of the recommendation results, and provide a feasible path for the implementation of intelligent clothing design assistant systems.
