Commonsense Knowledge Graph Completion (CKGC) aims to infer missing facts from the known commonsense knowledge. However, existing approaches still face the following two challenges, i.e., graph sparsity and noisy facts. First, entities in CKGs are generally sparse, which causes a vast of low-degree nodes lack enough contextual information. Second, CKGs are generally constructed by crowdsourcing, so there exist low-quality facts which degrades inference accuracy. To address these issues, we propose a novel framework CSGD for commonsense KG completion. The framework dynamically generates synthetics edges via graph densification strategy for low-degree entities. Extract high quality subgraph for reasoning. Additionally, we propose an adaptive dynamic weighted sampling strategy to improve the training efficiency. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed framework. The codes are publicly available.