Abstract: With the continuous deepening of globalization, third-party intellectual property (IP) core applications are becoming increasingly widespread. The gradual maturity of hardware Trojan attack technology makes it possible to implant hardware Trojan in the design process, posing a serious threat to the security of chip design. The hardware Trojan detection methods proposed in current work have the following drawbacks: relying on golden reference circuits, requiring complete test patterns, requiring a large number of samples for learning and so on. This paper proposes a graph neural network detection method based on controllability metrics for hardware Trojan detection requirements of IP cores. This method takes a gate level netlist as input, and first uses controllability values as guidance to obtain suspicious gate nodes for narrowing the search range; Then, the suspicious gate nodes are generated into corresponding subgraphs, and the graph convolutional neural network is used to extract features from the subgraphs, achieving detection of the subgraphs, and ultimately identifying the existence of hardware Trojans. This method eliminates the need for testing patterns and golden models, and improves the detection accuracy of hardware Trojans by combining their rare triggered features and structural characteristics. The experimental results show that the average True Positive Rate of this method is 100%, and the False Positive Rate is 0.75%. The results show that the proposed method can effectively reduce the false positive rate while ensuring a high true positive rate, achieving good detection results.