A bearing fault diagnosis method based on recurrence analysis and Stacking ensemble learning was proposed to effectively extract nonlinear information from rolling bearing signals and improve diagnostic accuracy. Firstly, the nonlinear information in bearing signals was mapped to a two-dimensional recurrence plot using recurrence analysis theory. Convolutional neural network and support vector machine models were established from the perspectives of image recognition and recurrence quantification analysis, respectively. Finally, the Stacking method was employed to integrate these two models, leveraging their respective strengths. Experimental results demonstrated that the proposed method significantly improved the classification accuracy of bearing vibration signals and exhibited excellent stability under varying load conditions, providing a reliable solution for bearing fault diagnosis.