Graph data have extensive applications in various domains, including social networks, biological reaction networks, and molecular structures. Graph classification aims to predict the properties of entire graphs, playing a crucial role in many downstream applications. However, existing graph neural network methods require a large amount of labeled data during the training process. In real-world scenarios, the acquisition of labels is extremely costly, resulting in labeled samples typically accounting for only a small portion of all training data, which limits model performance. Current semi-supervised graph classification methods, such as those based on pseudo-labels and knowledge distillation, still face limitations in effectively utilizing unlabeled graph data and mitigating pseudo-label bias issues. To address these challenges, we propose a Semi-supervised graph Contrastive learning based on Associative Memory network and Pseudo-label Similarity (SCoAMPS). SCoAMPS integrates pseudo-labeling techniques with contrastive learning by generating contrastive views through multiple encoders, selecting positive and negative samples using pseudo-label similarity, and defining associative memory network to alleviate pseudo-label bias problems. Experimental results demonstrate that SCoAMPS achieves significant performance improvements on multiple public datasets.