[目的/意义]分析并提出虚拟健康社区文本数据的知识发现策略，构建虚拟健康社区文本数据知识发现模型。[方法/过程]通过总结分析虚拟健康社区文本数据特点，针对其特点带来的数据挖掘困难制定相应的知识发现策略，并在DIKW体系指导下，依据提出的知识发现策略构建虚拟健康社区文本数据知识发现模型。通过应用计算机编码、自然语言处理技术、句法分析、制定推理规则等方法实现从自由文本数据到药物不良反应智慧的数据价值升华过程。[结果/结论]通过实证研究验证提出的知识发现策略和知识发现模型的有效性和可操作性，为后续虚拟健康社区文本数据知识发现的相关理论与实证研究提供参考。
[Purpose/significance] This study aims to analyze and propose the knowledge discovery strategy and build a knowledge discovery model of virtual health community text data. [Method/process] Firstly it summarized features of virtual health community text data, in view of the difficult of data mining to formulate the corresponding knowledge discovery strategy, and guided by DIKW system, to build knowledge discovery model of virtual health community text data based on knowledge discovery strategy. Through the application of computer code, natural language processing, syntactic analysis, and methods of inference rules, it realized the sublimation process of data value from free text data to the wisdom of adverse drug reactions. [Result/conclusion] Empirical research is carried out to verify the effectiveness and operability of the proposed knowledge discovery strategy and knowledge discovery model, so that it can provide reference for the subsequent theory and empirical research on knowledge discovery of virtual health community text data.
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