In recent years, load disaggregation methods based on deep learning have been widely applied. However, current researches mainly focuse on inputs from traditional Euclidean space sequences, which struggle to accurately capture temporal correlations during the operation of electrical devices, thereby reducing the resolution accuracy of electrical equipment analysis. Furthermore, the switching actions of household appliances may have long-distance impacts in time series data, yet existing models often overlook the long-distance dependency issues in load data. To address these challenges, a non-intrusive load disaggregation model is proposed based on graph convolutional network (GCN) and bidirectional long short-term memory (BiLSTM). This method transforms the total load sequence into graph-structured data containing nodes and edges using graph theory, effectively considering inter-node correlation features and utilizing GCN for feature extraction. Additionally, BiLSTM neural networks are introduced to handle the limitations of long-term time series data. Case analysis demonstrates that the proposed model significantly outperforms traditional methods in terms of disaggregation accuracy and effectiveness.