Academic anxiety is a negative emotional state experienced by learners of Chinese as a foreign language (CFL) during their Chinese language learning process. To explore the mediating mechanisms of positive and negative emotions and academic self-efficacy between teacher support and academic anxiety, a questionnaire survey was conducted among 1047 CFL learners, and a structural equation model was established to test the mediating effects. The study found that teacher support helps alleviate academic anxiety among CFL learners; teacher support exerts a certain inhibitory effect on learners’ academic anxiety. However, teacher support does not directly reduce academic anxiety but indirectly influences it through the independent mediating roles of self-efficacy and academic emotions, as well as through the chain mediating effect of both. Based on these findings, the paper provides recommendations for maintaining positive emotions, enhancing self-efficacy, implementing tailored teacher support, and constructing comprehensive motivational mechanisms for CFL learners.

This paper proposes incorporating linguistic semantic information into discourse relation recognition and constructing a Semantic Augmented Chinese Discourse Corpus (SACA) comprising 9546 adversative complex sentences. In adversative complex sentences, we suggest a quadruple (P, Q, R, $Q_{\beta }$) representing internal semantic elements, where the semantic opposition between Q and $Q_{\beta }$ forms the basis of the adversative relationship. P denotes the premise, and R represents the adversative reason. The overall annotation approach of this corpus follows the Penn Discourse Treebank (PDTB), except for the classification of senses. We combined insights from the Chinese Discourse Treebank (CDTB) and obtained eight sense categories for Chinese adversative complex sentences. Based on this corpus, we explore the relationship between sense classification and internal semantic elements within our newly proposed Chinese Adversative Discourse Relation Recognition (CADRR) task. Leveraging deep learning techniques, we constructed various classification models and the model that utilizes internal semantic element features, demonstrating their effectiveness and the applicability of our SACA corpus. Compared with pre-trained models, our model incorporates internal semantic element information to achieve state-of-the-art performance.