For pulmonary nodules detected in computed tomography (CT) images, it is necessary to automatically determine whether they are at the risk of canceration. This study proposes a multitask learning model based on the attention mechanism. Different from most existing research methods which only distinguish between the benignity and malignancy of nodules, the proposed model also assesses and outputs the semantic features related to the benignity and malignancy of nodules. The assessment of nine nodule features (subtlety, lobulation, spiculation, sphericity, margin, texture, calcification, diameter, and malignancy) and the sharing of internal characteristics are conducted at the same time to improve the performance of each subtask. The vision transformer (ViT) model is selected as the multitask shared feature extraction layer, and the whole model uses the dynamic weighted average method to optimize the Loss function of each subtask. Experiments on the LUNA16 dataset show that the proposed learning framework can improve the risk assessment of pulmonary nodule canceration and that the assessment of other semantic features can also enhance the interpretability of the results.