The advancement of social media has intensified interest in the research direction of Multimodal Sentiment Analysis (MSA). However, current methodologies exhibit relative limitations, particularly in their fusion mechanisms that overlook nuanced differences and similarities across modalities, leading to potential biases in MSA. In addition, indiscriminate fusion across modalities can introduce unnecessary complexity and noise, undermining the effectiveness of the analysis. In this essay, a Modal-Preserving and Interaction-Driven Fusion Network is introduced to address the aforementioned challenges. The compressed representations of each modality are initially obtained through a Token Refinement Module. Subsequently, we employ a Dual Perception Fusion Module to integrate text with audio and a separate Adaptive Graded Fusion Module for text and visual data. The final step leverages text representation to enhance composite representation. Our experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate that our model achieves state-of-the-art performance.