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Teacher feedback is the key to online collaborative discussion. To investigate the effects of different forms of teacher feedback intervention on learners' cognitive and emotional interactions in online collaborative discussion, this study collected collaborative discussion text data of online collaborative learners. Based on the framework of Community of Inquiry theory, naive Bayes algorithm for automatic coding of collaborative discussion text data was adopted. A bivariate (with or without emotion/guidance) experiment was designed based on teacher feedback. The participants of this study were college students (N = 109, average age = 20) of normal major participating in Teaching System Design. They were randomly divided into four experimental groups. In each experimental group, 4-5 people work in a group for collaborative learning. This study adopts quasi experimental research method, and the experiment period is 10 class hours. Reliability analysis, automatic text coding and ANOVA of cognitive-affective variables were used to conduct process mining for the collaborative discussion of four groups of learners by using heuristic mining algorithms. It found that different forms of teacher feedback have different effects on learners' cognitive emotion. Teachers' emotional feedback promotes learners' emotional interaction and cognitive interaction, whiccoch is easier to promote learners' cognitive interaction. Different forms of teacher feedback promote four types of cognitive emotion interaction process. This suggests that the multi-branch type of voice prompt feedback group has the best effect on learners' cognitive and emotional impact.