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Grassroots governance staff are submitting increasing amounts of text data to governance system due to encouragement of intelligent governance work. Misclassified work order data results in data loss, leading to wasted labor and material resources. Hence, effective text classification techniques are needed for correction. Training results are insufficient to suit industry needs due to limited work order data and difficult data conditions. For difficulties above, this research presents a mobileBERT-based preset class fusion text categorization algorithm (Preset class fusion BERT, PCFBERT). Templated preprocessing of address information, label representations produced by algorithm encoder are separated from input data representations to compute contrastive loss function and improve model performance on one hand and used as classifier input to produce final prediction results on the other. Traditional text classification algorithms cannot guarantee high speed and accuracy in face of tiny samples of actual data and complex data conditions. Suggested technique solves this problem. Templated preprocessing of address names reduces noise and makes dataset information more reliable. Label fusion and contrast learning techniques based on mobileBERT improve model data representation in this paper. Triple mapping module fuses pre-defined label class information into classifier and better utilize global information to improve decision-making. Combining strategies above improves structure robustness and performance without increasing model size. This research evaluates recommended strategy using simulated data from desensitized work orders and compares it to many popular text classification methods. Suggested method outperforms popular text classification algorithms in classifying work order text data. Same higher performance is achieved in two open Chinese multiclassification text datasets.
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