In recommendationsystem, cold-start issue is challenging due to the lack of interactions between new users or new items. Such issue could be alleviated via data-level and model-level strategies. Traditional data-level methods employ side information like feature information to enhance the learning of user and item embeddings. Recently, heterogeneous information networks (HINs) have been incorporated into the recommendationsystem as they provide more fruitful auxiliary information and meaningful semantics. However, these models are unable to capture the structural and semantic information comprehensively and neglect the unlabeled information of HINs during training. Model-level methods propose to apply the meta-learning framework which naturally fits into the cold-start issue, as it learns the prior knowledge from similar tasks and adapts to new tasks quickly with few labeled samples. Therefore, a contrastive meta-learning framework on HINs named CM-HIN is proposed, which addresses the cold-start issue in both data level and model level. Specifically, metapath and network schema views are exploredto describe the higher-order and local structural information of HINs. Within metapath and network schema views, contrastive learning is adopted to mine the unlabeled information of HINs and these two viewsare incorporated. Extensive experiments on three benchmark datasets demonstrate that CM-HIN outperforms all state-of-the-art baselines in three cold-start scenarios.