The combined models of Graph Neural Network (GNN) and Recurrent Neural Network (RNN) are widely used for patient similarity computation. However, these studies mainly use the medical concepts to organize patient graphs, while a lot of concepts in Electronic Medical Records (EMRs) are paratactic, learning the temporal information based on concept sequences may introduce noise to similarity computation. To address this problem, we propose an Event Graph Learning Network (EGLN) to learn patient similarity. Specially, we firstly leverage the trained Event Extraction (EE) model to obtain the event elements. Then, aggregating the paratactic concepts of each medical event to construct the event graph for the patient to avoid the negative influence of nonexistent temporal information between paratactic concepts. Finally, the spatial and temporal semantic information between event nodes is aggregated for similarity computation. We evaluate EGLN leveraging a real-world dataset, and the experiment results indicate that our proposed EGLN model outperforms all baselines.