Abstract: Small and medium-sized CNC machine tool firms have accumulated a large amount of fault maintenance data recorded in manual text during operation and maintenance. In order to accomplish efficient and accurate classification and help maintenance personnel carry out their work efficiently， this paper proposes a fault text classification and prediction approach based on convolution neural network and bi-directional long-short-term memory network. Firstly， the pre-processing is completed by creating a professional feature word database， and Word2Vec is used to train the word vector. Secondly， after the CNN layer extracts local features from the text vector， context features are extracted from the forward and backward LSTM. After the feature fusion and weighting of CNN and BiLSTM layers in the full connected layer， the full connected layer finds the output with the highest probability as the prediction result through the Softmax activation function， and presents the prediction accuracy of each category with the confusion matrix. Based on the fault data of an enterprise in the Yangtze River Delta， this paper makes an experimental analysis， and compares it with a single CNN and BiLSTM model. The experimental results indicate that the prediction accuracy of the new method is up to 94%， the average accuracy is increased by 11 percentage points， and the P value， R value and F value are all up to 95%， which can be used as an effective method in the field of small data volume fault text classification.

Key words: text classification, CNC machine tool fault, convolutional neural network, bi-directional long short-term memory