In recent years， liver cancer has become a disease that seriously threatens human health， and multi-modal ultrasound imaging is one of the important diagnostic tools for it. Similar to how clinicians use multi-modal ultrasound to diagnose liver cancer， using multi-modal fusion methods to integrate the image features of each ultrasound modality is expected to improve the accuracy of liver cancer diagnosis. However， the existing multi-modal fusion methods often isolate the feature information of each modality during the fusion process， failing to fully consider the intra-modal sample similarity and inter-modal semantic consistency， while ignoring modality uncertainty. Therefore， this paper proposes a liver cancer diagnosis method based on multi-modal ultrasound contrast learning， aiming to make full use of the feature information of each ultrasound modality to improve the diagnostic accuracy. Specifically， this method employs supervised contrastive learning to deeply explore modality features， capturing both the similarity information among samples within the modality and the semantic consistency information across different modalities. In addition， this method introduces a measure of modality uncertainty based on Subjective Logic， enabling dynamic fusion of modality information and exhibiting good robustness. Evaluation of multimodal ultrasound imaging shows that the proposed method achieves an 85.21% diagnostic accuracy， demonstrating performance improvement compared to other mainstream multimodal fusion methods.