Mutation testing is widely used to measure the test adequacy of a project. Despite its popularity, mutation testing is time-consuming and extremely expensive. To mitigate this problem, researchers propose Predictive Mutation Testing (PMT). Existing PMT approaches build classification models based on statistical program features or source code of programs to predict mutation testing results. Previous statistical feature-based PMT models need expensive overhead to collect dynamic features and neglect the rich information inherent in code text. Previous text-based PMT models extract essential code elements as input and outperform the feature-based models. However, they encode code text in a plain way. Therefore, they cannot sensitively capture subtle differences in the mutants and they have difficulty in capturing the correlation between the mutants and the tests. To address these challenges, we propose a new model, SODA. SODA uses a new learning strategy, Mutational Semantic Learning, to make our model spot code mutation and its impact on test behaviour. In particular, we employ a new sampling strategy to reinforce the corresponding relationship between mutant and test by sampling same-mutant contrastive group. Then we employ contrastive learning to make our model capture subtle differences in the mutants. We conduct experiment to investigate the performance of SODA. The results demonstrate that both in the cross-project and cross-version scenarios, SODA achieves the state-of-the-art classification performance (improves upon baselines by 5.32%-114.92% in kill-F1 score, 0.04%-25.54% in survive-F1 score, 4.25%-60.43% in accuracy) and has the lowest mutation score error.