With the development of big data and artificial intelligence, it is possible to transform the manual processing of patents into automated processing. In this study, combined with the advantages of Convolutional Neural Network (CNN) to extract local features and Two-way Long and Short Term Memory neural network (BiLSTM) to serialize and extract global features, the attention mechanism is introduced in the hidden layer of BiLSTM, and a BiLSTM_ATT_CNN combination model for Chinese patent text data is proposed. The BiLSTM_ATT_CNN combined model improves the accuracy of Chinese patent text classification by designing multiple comparison experiments.