A classification method based on deep learning was designed according to the characteristics of recording text and correlation data in order to improve the classification precision of the recording text with associated work order data. The embedding of the recording text and work order information was obtained through the bidirectional word embedding language model (ELMo). Local features of the sentence were mined by using convolutional neural networks (CNN) based on the word embedding. Title and description information of the work order were separately mined by using CNN. Features extracted by CNN were concatenated with a weighting factor. Then weighted features were entered into bidirectional gated recurrent unit (GRU) in order to capture the semantic features of the context. The attention mechanism was introduced to assign different weights to the output state of the GRU hidden layer. The experimental results show that the classification method has faster convergence rate and higher accuracy compared with the existing algorithms.