Aiming at the problems of incomplete intra-modal information， poor inter-modal interaction， and difficulty in training in multimodal sentiment analysis， a Multimodal Sentiment analysis network with Self-supervision and Multi-layer cross Attention fusion （MSSM） was proposed with Visual-and-Language Pre-training （VLP） model applied to the field of multimodal sentiment analysis. The visual encoder module was enhanced through self-supervised learning， and multi-layer cross attention was added to better model textual and visual features. Thus， the intra-modal information was made more abundant and complete， and the inter-modal information interaction was made more sufficient. Besides， the fast and memory-efficient exact attention with IO-awareness： FlashAttention was adopted in the proposed algorithm to address the high complexity of attention computation in Transformer. Experimental results show that compared with the current mainstream model Contrastive Language-Image Pre-training （CLIP）， MSSM improves the accuracy by 3.6 percentage points on the processed MVSA-S dataset and 2.2 percentage points on MVSA-M dataset， proving that the proposed network can effectively improve the integrity of multimodal information fusion while reducing computational cost.