A session-based recommendation has become a hot research topic, which seeks to recommend the next item based on anonymous behavior sequences in a short time. While previous methods have made many efforts to address the complex information relationships between items, we contend that they still suffer from two inherent limitations: 1) they fail to consider the noisy preference information typically contained in user behavior sequences and 2) they are unaware of the importance of complex high-order relationships between non-adjacent items. In light of this, we contribute a novel solution named CCL (short for Context-augmented Contrastive Learning ), which takes into account the joint effect of interest graph construction, context vectors, and contrastive learning. CCL decomposes session-based recommendation workflow into three steps. First, we adopt metric-based learning to reconstruct loose item sequences into tight item interest maps, making it easier to distinguish between the primary and secondary interests of users. Then, we propose adding a context vector to each session to provide a natural way to convey information beyond adjacent items. Finally, to improve the robustness of the model, we designed a contrastive self-supervised learning module as an auxiliary task to jointly learn the representation of items in the session. Extensive experiments have been conducted on two real-world datasets from different scenarios, demonstrating the superiority of CCL against several state-of-the-art methods.