At present, computed tomography (CT) are widely used to assist diagnosis. Especially, computer aided diagnosis (CAD) based on artificial intelligence (AI) is an extremely important research field in intelligent healthcare. However, it is a great challenge to establish an adequate labeled dataset for CT analysis assistance, due to the privacy and security issues. Therefore, this paper proposes a convolutional autoencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data, which only needs a small amount of labeled data for efficient feature learning. Through comprehensive experiments, it evaluates that the proposed scheme is superior to other approaches, which effectively solves the intrinsic labor-intensive problem during of artificial image labeling. Moreover, it verifies that the proposed convolutional autoencoder approach can be extended for similarity measurement of lung nodules images. Especially, the features extracted through unsupervised learning are also applicable in other related scenarios.