It is important to diagnose lung nodules as early as possible for sub sequent treatment.Although deep learning methods have achieved good results in tasks such as classification of malignant tumors,these methods do not provide m eaningful diagnostic functions resulting in the results obtained lack objectiven ess.More and more researchers have introduced other semantic features of lung n odules to solve this problem,but the introduction of multiple semantic features will cause negative migration of the model.In order to solve the problem of ne gative transfer caused by the different degrees of synchronized sharing of knowl edge between multiple semantic features of lung nodules.This paper proposes a d eep learning model based on uncertain multitask loss,classifies nine semantic f eatures of lung nodules (subtlety,internal structure,calcification,sphericity ,margin,lobulation,spiculation,texture and malignancy),and weighs multiple loss functions through the homogenous uncertainty of each task.We verified the method on the benchmark LIDC-IDRI dataset.The model proposed in this paper ha s a classification accuracy of 93.6% in malignancy,an area under the ROC curve o f 95.5%,a sensitivity of 84.6%,and a specificity of 94.5%.Our model improves the classification performance of malignancy by changing the relative weight of multiple semantic features through different semantic features of lung nodules.