Currently, research on multi-label text classification integrates label information. However, in the field of sentiment analysis, existing methods often overlook the correlations of labels based on the intensity and polarity of emotions themselves, which are crucial for accurate classification. To address these issues, this study proposes the MGE-BERT model which features multi-label interaction, graph enhancement, and emotion perception. The model first prioritizes sentiment label sorting through the correlations of sentiment intensity and hierarchy and then combines these sorted labels with text data as inputs into the BERT model. During this process, syntactic analysis techniques and sentiment lexicons are employed, and through a unique graph construction method, intricate dependency and emotion graphs are built. To further enhance the in-depth integration of label information and text features, the study uses BERT outputs as inputs to graph convolutional network (GCN), enabling it to capture and transmit contextual relationships between nodes more precisely. Experimental results demonstrate that the proposed MGE-BERT model outperforms state-of-the-art models, achieving improvements in Macro-F1 scores by 1.6% and 2.0% on the SemEval2018 Task-1C and GoEmotions datasets, respectively.