Event representation aims to transform individual events from a narrative event chain into a set of low-dimensional vectors to help support a series of downstream applications, e.g., similarity differentiation and missing event prediction. Traditional event representation models tend to focus on single modeling perspectives and thus are incapable of capturing physically disconnected yet semantically connected event segments. We, therefore, propose a heterogeneous event graph model (HeterEvent) to explicitly represent such event segments. Furthermore, another challenge in traditional event representation models is inherited from the datasets themselves. Data sparsity and insufficient labeled data are commonly encountered in event chains, easily leading to overfitting and undertraining. Therefore, we extend HeterEvent with a multistructure contrastive learning framework (MulCL) to alleviate the training risks from two structural perspectives. From the sequential perspective, a sequential-view contrastive learning component (SeqCL) is designed to facilitate the acquisition of sequential characteristics. From the graph perspective, a graph-view contrastive learning component (GraCL) is proposed to enhance the robustness of graph training by comparing different corrupted graphs. Experimental results confirm that our proposed MulCL [W+E] model outperforms state-of-the-art baselines. Specifically, compared with the previously proposed supervised model HeterEvent [W+E] [Zheng et al. (2020)], MulCL [W+E] shows an average improvement of 5.3% in terms of accuracy for the inference-ability-based tasks. For the representation-ability-based tasks, MulCL [W+E] achieves an average improvement of 2.7% in terms of accuracy for the hard similarity tasks and an improvement of 4.1% in terms of the Spearman's correlation for the transitive sentence similarity task, respectively.