The invention relates to a hardware Trojan horse detection method and system based on a bidirectional graph convolutional neural network. The method comprises the following steps of firstly, preprocessing a netlist file, creating a corresponding directed graph representation, encoding gate device information as a feature representation X, and constructing circuit directed graph data, respectively creating a forward circuit diagram for describing a circuit signal propagation structure and a reverse circuit diagram for describing a circuit signal dispersion structure, respectively constructing corresponding graph neural network feature extractors to extract structural features, and combining the structural features into final gate device features, constructing a multi-layer perceptron classification model, forming a hardware Trojan horse gate classification model by the multi-layer perceptron classification model and a graph neural network feature extractor, and learning model parameters by using a weighted cross entropy loss function to obtain a trained hardware Trojan horse gate classification model, and converting a to-be-detected netlist into a directed graph, inputting the directed graph into the trained hardware Trojan horse gate classification model for detection, and outputting a suspicious door device list. According to the method, the exit-level hardware Trojan horse can be effectively detected.