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Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.

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Data mining technology is an effective knowledge mining and data relationship induction technology based on massive data, which is widely used in data analysis in many fields. In order to improve the utilization effect of students’ performance and meet the teaching needs of modern education, data mining technology can be applied to the existing performance database to mine the data information and treatment. Data mining technology is used to analyse and process the data stored in the student achievement management system, which provides the basis for improving the teaching quality and optimizing the teaching resources. Based on the analysis of the relevant data of large-scale English test results, this paper finds out the relevant rules that affect college English test results, forms the corresponding performance prediction rules, uses data mining technology to more comprehensively analyse the factors that affect students’ performance, establishes a model, and uses data mining tools to mine and analyse students’ English test data. It is of great practical significance to select the model with high accuracy, further optimize the parameters, make good use of the data, and then take targeted measures to guide the teaching reform, help students make more efficient learning plans, and improve and perfect the existing problems in teaching.

Study of traditional Chinese medicine (TCM) syndromes is a key to the research of TCM modernization, and the core is the classification and diagnostic criteria of syndromes. The purpose of this article is to review the usage of classification algorithms of data mining in TCM syndrome researches, and comprehensively analyze the main features of algorithms and their applications. The appropriate classification algorithm should be chosen according to different research purposes. Rough sets and cluster analysis are suitable for exploratory research without requiring a prior knowledge. Fuzzy sets theory, neural networks and decision tree are suitable for syndrome diagnostic criteria research when the classification goal is clear, because they require a prior knowledge. Among them, fuzzy sets theory could be used in combination with other classification algorithms. Thus, some new methods such as fuzzy clustering, fuzzy rough sets or fuzzy decision tree might be more suitable for TCM algorithm classification research. It is suggested that some novel classification algorithms need to be developed to fit the condition of TCM syndrome, based on the interdisciplinary theories and technologies.