To realize digital asset management, improve industry operation efficiency, and promote the integration of power informationization, power companies need to implement effective data organization and management methods. This study proposes an efficient text type recognition model based on character-level features for the data in the electric power industry. In this model, characters are put through the BERT pre-training model to generate dynamic and efficient character vectors of the power customer service text. A BiLSTM network with the attention mechanism is used for the input of character vector sequences. The attention mechanism enables the effective capture of the latent features helpful for type recognition. Finally, we use the Softmax layer to recognize the power text type. The model proposed in this study achieves an accuracy of 98.81% on a data set of power customer service text, which is better than traditional neural network methods such as CNN and BiLSTM. It enhances the application of the BERT model and effectively solves the problem of semantic long-distance dependence in power text type recognition.