Research on Intelligent Classification of Service Hotline Work Orders Based on Semantic Recognition

[Objective] Traditional rail transit network service hotline mainly relies on manual customer service answering calls, manually filling out work orders and handling classifications. Passenger service staff undertake high-intensity and overloaded service work, while the service quality is difficult to be guaranteed. Therefore, it is necessary to introduce semantic recognition technology based on deep learning to achieve digitalized and intelligent operation management. [Method] The systematic requirements for the business classification of current service hotline and the intelligent classification of the work order are analyzed. The word- segmentation logic of semantic analysis is used and the recognition accuracy is improved by establishing a keyword library. Using such library as a domain dictionary, an intelligent text classification model based on distributed text vector representation and integrating the Transformer self-attention mechanism is constructed. In the proposed intelligent text classification model, the attention focus is more concentrated on the words strongly relevant to the classification task, thus reducing the interference of irrelevant words in the context on the classification results, and texts with different semantics in different contexts can also be dynamically displayed to achieve the classification of passengers′ intentions. On this basis, an intelligent classification system for hotline work order records is built. [Result & Conclusion] The experimental results on the real datasets show that the proposed intelligent text classification model has certain effectiveness and correctness. By adopting a highly modular software system design, the automatic classification of work orders is realized, which can effectively improve the work order response speed and reduce the costs of human and material resources. The proposed intelligent text classification model can improve the overall operation service quality and passenger satisfaction.