Aiming at the issues in fault diagnosis of energy Internet such as long model training time， insufficient extraction of fault features， and low diagnostic accuracy with limited training sample size， a Hierarchical Clustering and Multi-Head attention based Convolutional neural network （HCMHC） model was proposed. In the model， the novel Hierarchical Clustering （HC） model was adopted to reduce data redundancy effectively， while Convolutional Neural Network （CNN） and multi-head attention were combined for more accurate and comprehensive fault feature extraction. Furthermore， a contrastive learning model was employed to enhance the complementarity among features with limited training sample size， thereby improving model generalization ability and diagnostic accuracy on new data. Experimental verification results on the New England test system with 39 buses and 10 generators demonstrate that the HCMHC model achieves accuracies of 99.8% and 99.5% on two different datasets respectively， which have improvements of 4.3 and 4.5 percentage points approximately and respectively compared to the Multiple-Input CNN （MI-CNN） model. Additionally， even with a training set/validation set ratio of 20/80， this model still has accuracies of 98.3% and 95.8% on two datasets respectively. The above proves the significant effectiveness and superiority of the proposed model in the field of fault diagnosis.