Abstract: The 12345 Government Affairs Service Convenience Hotline is a public service platform set up by local governments to handle hotline events.In recent years,with the advancement of government digitization,the significance of the 12345 hotline as a communication link between citizens and government has greatly increased,and there are higher and higher requirements for the efficiency of event handling.Aiming at the problems that the traditional event dispatch method mainly relies on the manual operation of the dispatcher,which is slow in speed,low in accuracy,and consumes a lot of human resources,a government event dispatch method based on deep multi-view network is proposed.Firstly,we train the graph convolutional neural network with weights by self-supervised learning and extract the behavioral representations of event category-dispatched departments from the historical assignment records.After that,the BERT model fine-tuned by the government domain corpus is used to extract the semantic representation of the event description and event title.Then,the residual network based on the attention mechanism is used to fuse multiple views of the event to obtain the fusion representation of the event.Finally,the fusion representation is fed into the classifier to obtain the result of event dispatch.Experiments on the dataset of Nantong 12345 hotline show that the proposed method is superior to other baseline methods in terms of various metrics and can improve the efficiency of event dispatch.

Key words: 12345 hotline, Event dispatch, Text classification, Multi-view learning, Deep learning, Urban computing