Multimodal Sentiment Analysis (MSA) requires robust representations that capture both cross-modal consistency and intra-modal distinctions. Existing fusion methods often fail to adapt to diverse sentiment cues and neglect inter-modal correlations, while contrastive learning approaches insufficiently consider pair distribution and loss design. We propose an Adaptive Multi-scale Convolution fusion network with Contrastive Learning for multimodal sentiment analysis (AMCCL), which dynamically fuses multimodal information using an Adaptive Multiscale Convolution (AMC) module. The AMC module dynamically fuses features through multi-scale convolutions with adaptive weighting and squeeze-and-excitation block to enhance salient channels. Our fine-grained contrastive learning leverages sentiment polarity and intensity, with tailored loss functions to strengthen the positive pairs and balance the intermodal and intra-modal relations. Extensive evaluations on the MOSI and MOSEI datasets confirm that AMCCL delivers superior performance relative to state-of-the-art approaches.