Abstract: Daily safety inspection is an important means to maintain the safe operation of long-distance water diversion projects. At present, unstructured text data collected from patrol inspection mainly rely on manual safety level evaluation, which has obvious deficiencies in work efficiency and accuracy. Based on natural language processing technologies, this paper describes an intelligent text classification method of character oriented convolutional neural network (CNN). This method improves the CNN input layer by introducing a pre-trained single character vector, allowing the classification model to extract feature information directly from the text; it not just avoids dependency of traditional classification methods on the professional lexicon, but its results are not easily affected by the colloquial expressions and typographical errors in the text. Taking the inspection text of a domestic water diversion project as a test case, its effectiveness and superiority are verified through comprehensive comparison with several deep learning algorithms. Results show that the character level classification is much better than the traditional words based method, and CNN is significantly better than other deep learning networks in classification of the patrol inspection texts. Our method provides a new intelligent means with high classification efficiency and accuracy for the safety maintenance of water diversion projects.

Key words: water diversion project, text classification, character vectorization, convolution neural network (CNN), natural language processing