In the context of increasing global uncertainty and risk, Fabless supply chain is facing challenges such as inefficient design, outsourcing, packaging and testing collaboration, and lagging risk identification. Traditional statistical models are unable to meet demand due to limitations such as insufficient data integration and delayed response. This study constructs a multi-source heterogeneous supply chain knowledge graph that integrates design data, OEM information, logistics nodes, and risk events (data structuring is achieved through entity ID generation and relationship extraction), proposes a feature extraction method based on heterogeneous graph attention (defining meta paths such as "design agent test" to connect complex relationships between nodes, and combining node attention and semantic attention to generate entity embeddings), and develops an anomaly analysis model based on relationship graph neural network (risk probability prediction is achieved through relationship type driven inductive subgraph sampling and multi-layer convolutional encoder). The experimental results showed that the heterogeneous graph attention feature extraction method improved accuracy by 3.67% and AUC by 5.02% compared to the baseline method, and the relationship graph neural network anomaly analysis model improved accuracy by 5.23% and AUC by 3.53%, verifying the effectiveness of the two-stage method of "knowledge association risk perception" and accurately predicting risks such as supply chain delays and quality anomalies. Research has found that knowledge graphs can mine hidden associations by semantically integrating multi-source data. The collaboration between heterogeneous graph attention and relationship graph neural networks can enhance feature representation and risk perception capabilities, effectively solving the problems of insufficient data integration and lagging recognition in traditional methods. The Fabless supply chain knowledge management algorithm and alarm mechanism proposed in this study provide interpretable and scalable intelligent decision support for enterprises by mining implicit associations through knowledge association and dynamically evaluating abnormal probabilities through risk perception. In the future, it can further integrate unstructured data to expand graph coverage and explore cross domain application promotion.

Ming Li. Design of Fabless Supply Chain Knowledge Management Algorithm and Alarm Mechanism Combining Knowledge Association and Risk Perception. International Journal of Business Management and Economics and Trade (2025), Vol. 6, Issue 1: 160-168. https://doi.org/10.38007/IJBMET.2025.060116.

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