| 基于DBM的电力投诉工单分类的应用研究 |
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| 引用本文：杨恒?覮，颜宏文.基于DBM的电力投诉工单分类的应用研究[J].计算技术与自动化,2020,(3):86-90 |
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| 中文摘要:提出了基于深度玻尔兹曼机的电力投诉工单识别分类模型。首先对投诉工单数据进行数据清洗，对处理后的数据使用结巴分词算法进行分词并制作字典，再使用词袋模型对所分词向量化处理提取文本特征。进一步地，通过TF-IDF算法找出关键词以及余弦相似度计算训练、测试文档间的相似度；最后使用深度玻尔兹曼机对投诉工单进行分类。实验证明，分类的准确度达到80%，有效地缓解电力部门的工作压力，提高工作效率。 |
| 中文关键词:投诉 TF-IDF DBM 文本分类 |
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| Application Research on Classification of Power Complaint Work Order Based on DBM |
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| Abstract:A classification model based on the deep Boltzmann machines for power complaint work order identification is proposed. Firstly，the data of the complaint work order data is cleaned and use the Jieba algorithm to segment the processed data，and create a dictionary. Then the BoW model is used to extract the text feature from the segmentation vectorization process. Further，the TF-IDF algorithm is used to find keywords and cosine similarity calculations to calculate the similarity between training and test documents.Finally，the deep Boltzmann machine is used to classify the complaint work orders. The experiment proves that the accuracy of the classification reaches 80%，effectively alleviating the work pressure of the power sector and improving work efficiency. |
| keywords:complaint TF-IDF deep Boltzmann machine text classification |
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