The current dialogue emotion recognition models often overlook the coherence features and discourse structure information in context modeling. Therefore, this paper proposes a dialogue emotion recognition model based on coherence features and discourse structure. Firstly, discourse coherence detection is conducted to eliminate weak or incoherent discourse information, and both local and global coherent information are obtained by constructing a coherence matrix. Secondly, a dialogue parser is utilized to establish discourse structure relations, and a directed acyclic graph is employed to model the discourse structure while conveying both discourse structure information and speaker information. Finally, through interactive attention, coherent information and discourse information are interactively integrated to generate emotional labels. This paper validates the proposed model using two public datasets, with results indicating that compared to existing models, the proposed model demonstrates certain improvements in performance indices.