<p>Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to integrate textual and visual modalities to discern sentiment polarity associated with specific opinion aspects. However, existing approaches struggle with semantic alignment, fine-grained cross-modal interaction, and modality fusion due to discrepancies in expression patterns and feature granularity between modalities. To address these challenges, this study proposes an effective adaptive model (CMCL-KMSE) that integrates cross-modal multi-anchor contrastive learning and knowledge-guided multi-view semantic enhancement. First, the cross-modal alignment optimizer in CMCL-KMSE employs a multi-anchor contrastive learning strategy to generate high-quality initial alignment semantics, thereby improving modality coherence. Next, to augment fine-grained sentiment perception pertinent to opinion aspects, the sentiment-driven modal interaction module incorporates prior sentiment knowledge via attention mechanisms, guiding the model to focus on aspect-aware sentiment-relevant textual and visual segments. Additionally, the vision-driven aspect-aware enhancement module refines the model’s attention to aspect-related regions by leveraging interactions between aspect terms and adjective-noun pairs (ANPs) extracted from images, thus mitigating granularity mismatches between modalities. Finally, the soft-routing fusion module dynamically assigns adaptive weights to different modal interaction features, emphasizing their contributions for accurate sentiment classification. Extensive experiments on two benchmark datasets (Twitter-2015 and Twitter-2017) demonstrate that CMCL-KMSE achieves competitive performance in terms of accuracy and Macro-F1, validating its effectiveness in complex sentiment understanding scenarios.</p>