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This paper proposes an enhanced quantum-inspired sentiment analysis model incorporating a self-embedding mechanism for sentiment feature extraction and classification tasks. The method integrates phase-pre-trained self-embedding, bidirectional GRUs, a multi-head attention mechanism, and a multi-layer Transformer structure, effectively capturing semantic and emotional features in texts. Simultaneously, the model introduces contrastive learning and an enhanced feature interaction module, further improving feature discriminability. Extensive experiments on the RECCON dataset demonstrate that the proposed model significantly outperforms mainstream baseline methods (KEC, MPEG, Window Transformer) on key metrics such as macro-F1, positive-class F1, and negative-class F1. The experimental results show that the method not only improves overall accuracy and recall but also effectively mitigates challenges arising from class imbalance, achieving a macro-F1 of 0.95, positive-class F1 of 0.93, and negative-class F1 of 0.97 on the test set. The findings suggest that the combination of quantum-inspired structures and self-embedding mechanisms holds broad application prospects for complex sentiment analysis tasks.