Record of power work orders in substation area serves as a reflection of substation operational conditions and user requirements， and is an important basis for establishing substation’s electricity safety management system and meeting the electricity demands of users. To address the issues of power work order classification in substation areas brought by high complexity and strong professionalism of the orders， a power work order classification in substation area， Mini RoBERTa-Long Short-Term Memory-Graph Attention neTwork （MiniRBT-LSTM-GAT） was proposed. Label Smoothing （LS） and a pre-trained language model were integrated by the proposed model. Firstly， a pre-trained model was utilized to calculate the character-level feature vector representation in the power work order text. Secondly， Bidirectional Long Short-Term Memory （BiLSTM） network was employed to capture the dependency within the power text sequence. Thirdly， Graph Attention neTwork （GAT） was applied to emphasize the feature information that contributes to text classification significantly. Finally， LS was used to modify the loss function， so as to improve the classification accuracy of the model. The proposed model was compared with mainstream text classification algorithms on Power Work Order dataset in Rural power Station area （RSPWO）， 95598 Power Work Order dataset in ZheJiang province （ZJPWO）， and THUCNews （TsingHua University Chinese News） dataset. Experimental results show that compared with Bidirectional Encoder Representations from Transformers （BERT） model for Electric Power Audit Text classification （EPAT-BERT）， the proposed model has an increase of 2.76 percentage points in precision and 2.02 percentage points in F1 value on RSPWO， and has an increase of 1.77 percentage points in precision and 1.40 percentage points in F1 value on ZJPWO. In comparison with capsule network based on BERT and dependency syntax （BRsyn-caps）， the proposed model has an increase of 0.76 percentage points in precision and 0.71 percentage points in accuracy on THUCNews dataset. The above confirms the effectiveness of the proposed model in enhancing the classification performance of power work orders in substation area， and the good performance of the proposed model on THUCNews dataset， verifying the generality of the model.