Abstract: With the development of national digital construction,the intellectualization and specialization of social governance have become the basic requirements for the progress of urban science and technology.All government systems need to deal with the demands of people efficiently and accurately.However,the public appeal information collected from the appeal channels of major government portals is manually judged by responsible departments and then manually assigned to relevant departments for follow-up verification and processing,which greatly limits the efficiency and accuracy of appeal processing.Using artificial intelligence and deep learning methods,the intelligent dispatching algorithm based on real public demand information data training,accurately and efficiently dispatches demands to relevant departments,accelerates the speed of government affairs processing process and greatly reduces unnecessary labor costs.Therefore,the research of this scheduling algorithm is of great significance.First,the data is denoised and desensitized,and hierarchical stitching is used to build data labels and standard process libraries for label alignment.Then,a baseline model for address recognition is trained on publicly available datasets,and a label fusion method based on category proportion sampling is proposed to solve the problem of imbalanced data in work order classification.Experimental results show that the method improves the baseline model by varying degrees.Finally,combining the classification model and the address recognition model,an intelligent response template is constructed to complete the entire process of intelligent dispatching for complaint handling.

Key words: Intelligent order dispatch, Class imbalance, Label combination, BERT model, Deep learning