贝叶斯网模型提供了一种方便和直观的框架结构来表示变量间的关系，非常适合在诊断测验中对教育评估的内容进行建模。本研究将两种贝叶斯网分类模型与序列多级计分诊断模型S-GDINA进行综合比较。考察两种贝叶斯网分类模型与S-GDINA在Q矩阵正确界定和包含一定比例(25%、30%)的错误时，两者对被试的分类性能；并将贝叶斯网分类模型应用到实证数据中，展示贝叶斯网分类模型在实证数据中的分类过程和分类性能。研究结果表明：当Q矩阵由专家正确界定时，朴素贝叶斯分类模型的分类效果与S-GDINA模型相差不大，同样可以达到很好的分类效果，树增广的朴素贝叶斯分类模型的分类性能也能达到良好。实证结果进一步表明，将贝叶斯网分类模型应用于教育测量领域中的诊断分类工具是有其优势和可行的，尤其是当测验数据对于所选用诊断模型的拟合较差、测验的Q矩阵中包含错误或测验数据中包含较多的噪音时。

In recent years, Bayesian networks have been widely used in the field of artificial intelligence, but have received relatively little attention in the field of psychology. Most of the existing studies have applied Bayesian networks to binary items. This research combines Bayesian networks with polytomous items, which has important theoretical and practical implications. Two Bayesian network classification models were comprehensively compared with the sequential-GDINA (S-GDINA). The results showed that the classification performance of the naive Bayesian classifier (NBC) was comparable to that of the S-GDINA model, which could achieve equally good classification performance. Also, the classification performance of the tree augmented naive Bayesian classifier (TAN) could achieve good classification performance when the percentage of errors contained in the Q matrix was 0% (the Q matrix was correctly defined by the expert). Both the NBC and the TAN had better classification results in the polytomous scored test. The response classification consistency of the NBC and TAN reached 84% and 71%, respectively, indicating that Bayesian networks can be well applied to polytomous scored items.