The performance of machine learning algorithms depends on the distance metric, in addition to the model and loss function, etc. The partial label metric learning technique can improve the accuracy of the algorithm by using the training data to learn a better distance metric, which has gradually attracted the attention of scholars in recent years. However, the essence of partial label learning is mainly to deal with multi-classification problems, while class imbalance is a common phenomenon in multi-classification problems. The class imbalanced problem affects the prediction accuracy of minority class samples, but the current partial label metric learning algorithms rarely consider the class imbalanced problem. In this paper, we propose two partial label metric learning algorithms (PLCCML-SFN and PLCCML-LDD) that can solve the class imbalanced problem. The basic idea is to add a regularization term to the objective function of the PL-CCML model, which can induce each class to be uniformly distributed in the new metric space and thus play the role of balancing each class. The experimental results show that these two algorithms, compared with the existing partial label metric learning algorithms, have improved the overall performance on the class imbalanced data, especially the determination accuracy of the minority class samples has improved substantially.