[目的/意义]医疗电子病历作为重要的医疗数字资源，蕴含着丰富的临床信息和知识，对其进行深入的知识挖掘和发现，能够发现隐性医疗知识，为医学临床提供准确的预测和决策支持。[方法/过程]文章探讨了电子病历知识发现方法框架，提出基于主成分分析法的加权关联规则算法的电子病历知识发现实现过程，并以脑血管疾病患者住院电子病历进行实证研究。[结果/结论]文章提出的加权关联规则方法在脑血管共病模式识别方面具有一定的优化作用，通过加权处理后，关联规则及其置信度都有新的变化。该加权关联规则方法在电子病历知识发现中具有应用价值和前景，也为未来研究和实践提供了新的思路和方法指导。

[Purpose/significance] As an important medical digital resource, medical electronic medical record con⁃tains rich clinical information and knowledge, and its in-depth knowledge mining and discovery can discover tacit med⁃ical knowledge and provide accurate prediction and decision support for medical clinics. [Method/process] This paper discusses the framework of knowledge discovery methods for electronic medical records, proposes the implementation process of electronic medical record knowledge discovery based on the weighted association rule algorithm of principal component analysis, and conducts empirical research with the inpatient electronic medical records of patients with cere⁃brovascular diseases. [Result/conclusion] It is found that the weighted association rule method proposed in this study has some optimization effect in cerebrovascular co-morbidity pattern recognition, and the association rule and its confi⁃dence level are newly changed through the weighting process. The results demonstrate the application value and pros⁃pect of this weighted association rule method in electronic medical record knowledge discovery, and also provide new ideas and methodological guidance for future research and practice.