A speech emotion recognition network based on unsupervised graph contrastive learning (SERUGCL) was proposed to address the issues of sparse labeled data and difficulties in modeling high-dimensional speech features in most speech datasets. This method was trained using unlabeled data. Firstly, an original view of speech features was constructed based on feature similarity, and the graph structure was utilized to model the dependencies between speech frames, thereby alleviating the computational pressure caused by directly modeling high-dimensional features. Then, two enhanced views were generated through a combination of the fast gradient sign method (FGSM) and subgraph sampling-edge perturbation. All views were processed by a differentiated encoder, and a weighted pooling mechanism was adopted to obtain the global embedding. Finally, support vector machine (SVM) was used for emotion classification. The SERUGCL model achieved unweighted accuracy (UA) of 69.96% and weighted accuracy (WA) of 70.24% on the IEMOCAP dataset, and UA of 91.04% and WA of 90.29% on the EMO-DB dataset. Compared with DSTCNet, the UA and WA of SERUGCL improved by 8.18 and 8.44 percentage points on IEMOCAP and by 4.49 and 1.50 percentage points on EMO-DB datasets respectively. The results of comparative and ablation experiments also verified the effectiveness of the model.