Abstract: Fault classification methods for gas drainage pumps based on single-component features fail to consider the interactions among different components, making it difficult to accurately capture the essential characteristics of faults and thereby limiting their accuracy and reliability in gas drainage pump fault classification tasks. To address this issue, a gas drainage pump fault classification model based on fused multi-component joint features using Graph Sampling and Aggregation with a Hierarchical Attention Mechanism (GraphSAGE-HAT) was proposed. First, vibration data from the bearings, impeller, and pump casing of the gas drainage pump were collected using acceleration sensors to construct a dataset containing features from three components, and the dataset was preprocessed using the Min–Max normalization method. Second, the preprocessed dataset was transformed into a homogeneous graph using the K-Nearest Neighbor (KNN) algorithm to facilitate graph feature learning by graph neural network algorithms. Then, a hierarchical attention mechanism (HAT) was introduced to construct the GraphSAGE-HAT algorithm, in which HAT performed hierarchical weighted aggregation of intra-node component features as well as node and neighboring-node features in the homogeneous graph, effectively capturing inter-component correlation features and inter-node data structural characteristics. Finally, the aggregated features were fed into a fully connected layer to achieve gas drainage pump fault classification. Experimental results showed that, compared with single-component features, the model combining fused multi-component joint features with the GraphSAGE algorithm improved classification accuracy by 9.24%. With the further introduction of HAT, the model based on fused multi-component joint features and the GraphSAGE-HAT algorithm achieved an accuracy of 98.08%.