数据库技术越来越成熟，应用非常广泛，存储的数据量越来越大。大多数数据往往只是简单处理和应用，并没有进行数据深度挖掘，造成数据资源的浪费。关联规则算法可以有效地挖掘出数据中不同事务之间的微小关系，教学评价中，教师职称、教师年龄、教师教学时长与教学态度、教学纪律、教学方法、教学效果、师德教风等方面密切相关。通过apriori算法和Fp-growth算法两个典型关联规则算法的分析研究，详细叙述两种算法的题解过程，利用两种算法来研究Y大学的教学评价数据，发现高职称年长教师的授课效果较好，但是学生的认可度较低，年轻教师的授课效果不太好，学生的认可度反而较高，说明老教师在沟通交流方面不满足青年学生的期望，青年教师的教学能力和教学方法有待提升。
Database technology is becoming more and more mature, widely used, and the amount of data stored is increasing. Most data are often simply processed and applied without deep data mining, resulting in a waste of data resources. Association rule algorithm can effectively mine the small relationship between different transactions in the data. In teaching evaluation, teachers’ professional title, teachers’ age and teachers’ teaching time are closely related to teaching attitude, teaching discipline, teaching methods, teaching effect, teachers’ ethics and teaching style. Through the analysis and research of two typical association rule algorithms, apriori algorithm and FP-growth algorithm, the problem solving process of the two algorithms is described in detail. The two algorithms are used to study the teaching evaluation data of Y University. It is found that the teaching effect of senior teachers with high professional titles is better, but the recognition of students is low, the teaching effect of young teachers is not very good, and the recognition of students is higher, it shows that the old teachers do not meet the expectations of young students in communication, and the teaching ability and teaching methods of young teachers need to be improved.
