Cross-project defect prediction (CPDP) has emerged as a crucial research area in software engineering and data mining. Using defective code from other data-rich projects to build prediction models solves the problem of insufficient data during model construction. However, the distribution difference between the code files of source and target projects results in poor cross-project prediction. Most studies adopt the domain adaptation methods to solve this problem, but the existing methods only focus on the influence of conditional or marginal distribution on domain adaptation, ignoring its dynamics. On the other hand, they fail to choose appropriate pseudo-labels. Based on the above two aspects, this study proposes a cross-project defect prediction method based on dynamic distribution alignment and pseudo-label learning (DPLD). Specifically, the proposed method reduces the marginal and conditional distribution differences between projects in the domain alignment and category alignment modules, respectively, by means of the adversarial domain adaptation method. Additionally, it dynamically and quantitatively characterizes the relative importance of the two distributions using dynamic distribution factors. Furthermore, this study proposes a pseudo-label learning method to enhance the accuracy of pseudo-labels as real labels through the geometric similarity between data. Experiments conducted on the PROMISE dataset show that DPLD achieves average improvements of 22.98% and 15.21% in terms of F-measure and AUC, respectively. These results demonstrate the effectiveness of the DPLD method in reducing distribution differences between projects and improving the performance of cross-project defect prediction.