1188 工程科学学报，第43卷.第9期 pattern of knowledge graph of TCM classic prescriptions.ChinJ Univ Nat Sci Ed,2020,43(1):65 Inf Tradit Chin Med,2019,26(8):94 (李德玉，罗锋，王素格.融合CNN和标签特征的中文文本情绪 (尹丹，周璐，周雨玫，等.中医经方知识图谱“图搜索模式”设计 多标签分类.山西大学学报（自然科学版），2020,43(1)：65) 研究.中国中医药信息杂志，2019,26(8)：94) [11]Joulin A,Grave E,Bojanowski P,et al.Bag of Tricks for Efficient [3]Liu C.Gao JL,Dong Y,et al.Study on TCM syndrome Text Classification Il Proceedings of the 15th Conference of the differentiation and diagnosis model based on BP neural network European Chapter of the Association for Computational for syndrome elements and their common combinations in patients Linguistics.Valencia,2017:427 with borderline coronary lesion.Chin J Inf Tradit Chin Med,2021, [12]Yi S X,Yin H P,Zheng H Y.Public security event trigger 28(3):104 identification based on Bidirectional LSTM.Chin J Eng,2019, (刘超，高嘉良，董艳，等.基于BP神经网络的冠状动脉临界病变 41(9):1201 患者证候要素及其常见组合中医辨证诊断模型研究.中国中医 (易士翔，尹宏鹏，郑恒毅.基于BiLSTM的公共安全事件触发词 药信息杂志，2021,28(3)：104) 识别.工程科学学报，2019,41(9)：1201) [4]Chu N.Research on Hybrid Intelligent Based Syndrome [13]Chen G B,Ye D H,Xing Z C,et al.Ensemble application of Differentiation System for Traditional Chinese Medicine convolutional and recurrent neural networks for multi-label text [Dissertation].Shanghai:Shanghai Jiaotong University,2012 categorization /2017 International Joint Conference on Neural (褚娜.基于混合智能的中医辨证系统研究学位论文]上海：上 Nenworks (IJCNN).Anchorage,2017:2377 海交通大学，2012) [14]Yogatama D,Dyer C,Ling W,et al.Generative and discriminative [5]Yang K M.Research on Clinical Data Mining Technology of text classification with recurrent neural networks[J/OL].ArXi Diabetes TCM [Dissertation].Kunming:Kunming University of Preprin (2017-03-06)[2020-12-29].https://arxiv.org/abs/1703. Science and Technology,2013 01898v1 (杨开明.糖尿病中医临床数据挖掘技术研究学位论文].昆明： [15]Wang B X.Disconnected Recurrent Neural Networks for Text 昆明理工大学，2013) Categorization Il Proceedings of the 56th Anmual Meeting of [6]Zhou L,Li GG,Sun Y,et al.Construction of intelligent syndrome the Association for Computational Linguistics.Melbourne,2018: differentiation and formula selection of compound structure model 2311 World Chin Med,2018,13(2):479 [16]Kim Y.Convolutional Neural Networks for Sentence (周璐，李光庚，孙燕，等.复合结构智能化辨证选方模型的构建 Classification Il Proceedings of the 2014 Conference on Empirical 世界中医药，2018.13(2)：479) Methods in Natural Language Processing (EMNLP).Doha,2014: [7] Shu X,Cao Y,Huang X,et al.Construction of prediction model of 1746 qi deficiency syndrome in acute ischemic stroke based on neural [17]Mikolov T,Sutskever I,Chen K,et al.Distributed representations network analysis technique.Glob Tradit Chin Med,2019,12(11): of words and phrases and their compositionality[J/OL].arXiv 1650 preprint (2013-10-16)[2021-5-22].https://arxiv.org/abs/1310. (舒鑫，曹云，黄幸，等.基于神经网络分析技术的急性缺血性卒 4546 中气虚证预测模型构建的研究.环球中医药，2019,12(11)： [18]Pennington J,Socher R,Manning C.Glove:Global Vectors for 1650) Word Representation /Proceedings of the 2014 Conference on [8]Shen C B,Wang Z H,Sun Y G.A multi-label classification Empirical Methods in Natural Language Processing (EMNLP). algorithm based on label clustering.Compur Eng Softe,2014, Doha2014:1532 35(8):16 [19]Vaswani A,Shazeer N,Parmar N,et al.Attention is all you need (申超波，王志海，孙艳歌.基于标签聚类的多标签分类算法.软 [J/OL].arXiv preprint (2017-6-12)[2021-5-22].https://arxiv.org/ 件，2014,35(8)：16) abs/1706.03762 [9]Huang Z Q.Multi-Label Classification and Label Completion [20]Devlin J,Chang M W,Lee K,et al.BERT:Pre-Training of Deep Algorithm Based on K-Means [Dissertation].Anqing:Anqing Bidirectional Transformers for Language Understanding./ Normal University,2020 Proceedings of the 2019 Conference of the North American (黄志强.基于K-means的多标签分类及标签补全算法[学位论 Chapter of the Association for Computational Linguistics. 文].安庆：安庆师范大学，2020) Minneapolis,Minnesota,2018:4171 [10]Li D Y,Luo F,Wang S G.A multi-label emotion classification [21]Yang Z L,Dai Z H,Yang Y M,et al.XInet:Generalized method for Chinese text based on CNN and tag features.Shanxi autoregressive pretraining for language understanding[J/OL].pattern of knowledge graph of TCM classic prescriptions. Chin J Inf Tradit Chin Med, 2019, 26（8）: 94 （尹丹, 周璐, 周雨玫, 等. 中医经方知识图谱“图搜索模式”设计 研究. 中国中医药信息杂志, 2019, 26（8）：94） Liu  C,  Gao  J  L,  Dong  Y,  et  al.  Study  on  TCM  syndrome differentiation  and  diagnosis  model  based  on  BP  neural  network for syndrome elements and their common combinations in patients with borderline coronary lesion. Chin J Inf Tradit Chin Med, 2021, 28（3）: 104 （刘超, 高嘉良, 董艳, 等. 基于BP神经网络的冠状动脉临界病变 患者证候要素及其常见组合中医辨证诊断模型研究. 中国中医 药信息杂志, 2021, 28（3）：104） [3] Chu  N. Research on Hybrid Intelligent Based Syndrome Differentiation System for Traditional Chinese Medicine [Dissertation]. Shanghai: Shanghai Jiaotong University, 2012 （ 褚娜. 基于混合智能的中医辨证系统研究[学位论文]. 上海: 上 海交通大学, 2012） [4] Yang  K  M. Research on Clinical Data Mining Technology of Diabetes TCM [Dissertation].  Kunming:  Kunming  University  of Science and Technology, 2013 （ 杨开明. 糖尿病中医临床数据挖掘技术研究[学位论文]. 昆明: 昆明理工大学, 2013） [5] Zhou L, Li G G, Sun Y, et al. Construction of intelligent syndrome differentiation and formula selection of compound structure model. World Chin Med, 2018, 13（2）: 479 （周璐, 李光庚, 孙燕, 等. 复合结构智能化辨证选方模型的构建. 世界中医药, 2018, 13（2）：479） [6] Shu X, Cao Y, Huang X, et al. Construction of prediction model of qi  deficiency  syndrome  in  acute  ischemic  stroke  based  on  neural network analysis technique. Glob Tradit Chin Med, 2019, 12（11）: 1650 （舒鑫, 曹云, 黄幸, 等. 基于神经网络分析技术的急性缺血性卒 中气虚证预测模型构建的研究. 环球中医药, 2019, 12（11）： 1650） [7] Shen  C  B,  Wang  Z  H,  Sun  Y  G.  A  multi-label  classification algorithm  based  on  label  clustering. Comput Eng Softw,  2014, 35（8）: 16 （申超波, 王志海, 孙艳歌. 基于标签聚类的多标签分类算法. 软 件, 2014, 35（8）：16） [8] Huang  Z  Q. Multi-Label Classification and Label Completion Algorithm Based on K-Means [Dissertation].  Anqing:  Anqing Normal University, 2020 （ 黄志强. 基于K-means的多标签分类及标签补全算法[学位论 文]. 安庆: 安庆师范大学, 2020） [9] Li  D  Y,  Luo  F,  Wang  S  G.  A  multi-label  emotion  classification method for Chinese text based on CNN and tag features. J Shanxi [10] Univ Nat Sci Ed, 2020, 43（1）: 65 （李德玉, 罗锋, 王素格. 融合CNN和标签特征的中文文本情绪 多标签分类. 山西大学学报(自然科学版), 2020, 43（1）：65） Joulin A, Grave E, Bojanowski P, et al. Bag of Tricks for Efficient Text  Classification  // Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Valencia, 2017: 427 [11] Yi  S  X,  Yin  H  P,  Zheng  H  Y.  Public  security  event  trigger identification  based  on  Bidirectional  LSTM. Chin J Eng,  2019, 41（9）: 1201 （易士翔, 尹宏鹏, 郑恒毅. 基于BiLSTM的公共安全事件触发词 识别. 工程科学学报, 2019, 41（9）：1201） [12] Chen  G  B,  Ye  D  H,  Xing  Z  C,  et  al.  Ensemble  application  of convolutional  and  recurrent  neural  networks  for  multi-label  text categorization  //  2017 International Joint Conference on Neural Networks (IJCNN). Anchorage, 2017: 2377 [13] Yogatama D, Dyer C, Ling W, et al. Generative and discriminative text  classification  with  recurrent  neural  networks[J/OL]. ArXiv Preprin (2017-03-06)  [2020-12-29]. https://arxiv.org/abs/1703. 01898v1 [14] Wang  B  X.  Disconnected  Recurrent  Neural  Networks  for  Text Categorization  // Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.  Melbourne,  2018: 2311 [15] Kim  Y.  Convolutional  Neural  Networks  for  Sentence Classification // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, 2014: 1746 [16] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of  words  and  phrases  and  their  compositionality[J/OL]. arXiv preprint (2013-10-16)  [2021-5-22]. https://arxiv.org/abs/1310. 4546 [17] Pennington  J,  Socher  R,  Manning  C.  Glove:  Global  Vectors  for Word  Representation  // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, 2014: 1532 [18] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [J/OL]. arXiv preprint (2017-6-12)  [2021-5-22]. https://arxiv.org/ abs/1706.03762 [19] Devlin J, Chang M W, Lee K, et al. BERT: Pre-Training of Deep Bidirectional  Transformers  for  Language  Understanding.  // Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. Minneapolis, Minnesota, 2018: 4171 [20] Yang  Z  L,  Dai  Z  H,  Yang  Y  M,  et  al.  Xlnet:  Generalized autoregressive  pretraining  for  language  understanding[J/OL]. [21] · 1188 · 工程科学学报，第 43 卷，第 9 期