The process of training a deep learning model requires many annotation samples, even though annotation data is difficult to obtain in the medical field. A self-supervised learning algorithm combined with partial annotation data is proposed as a solution to this problem, in order to improve classification performance of 3D pulmonary nodules. Based on the traditional self-supervised training network structure, a multitask learning network structure is designed to address a large amount of unannotated data and a small amount of annotated data obtained from medical image processing tasks. First, the proposed algorithm trains the unannotated data, and then explores the annotation data to continuously train the model. Thus, this algorithm manages to share partial network structures and parameters between the annotated and unannotated data. Compared to traditional self-supervised learning methods, the proposed algorithm can learn to recognize the discriminant features of pulmonary nodules to ensure the model's capacity to generalize, therefore, model transfer learning can also perform better when applied to the classification of pulmonary nodules. The classification accuracy of the proposed algorithm on LIDC-IDRI dataset is 0.886, and the area under the curve (AUC) is 0.929. The results of the investigation indicate that the proposed algorithm can effectively improve classification performance of pulmonary nodules.