Unsupervised representation learning is a primary method for extracting distinguishable shape information from unlabeled point cloud data. Existing approaches capture global shape features of whole point clouds but often overlook local part-level details and are computationally expensive due to their reliance on whole point clouds and numerous negative samples. Inspired by the human visual mechanism of perceiving whole objects from local shapes, this study proposes an unsupervised part-level learning network, called reconstruction contrastive part (Rc-Part). First, a dataset of 40000 part point clouds is constructed by preprocessing public whole point cloud datasets. Then, Rc-Part employs contrastive learning without negative samples to capture distinguishable semantic information among parts and uses an encoder-decoder architecture to learn part structure information. Joint training with both contrastive and reconstruction tasks is then conducted. Finally, the encoder learned from the point cloud dataset is directly applied to whole-shape classification. Experiments on the PointNet backbone achieve high classification accuracies of 90.2% and 94.0% on the ModelNet40 and ModelNet10 datasets, respectively. Notably, despite containing 10000 fewer samples than the ShapeNet dataset, the part dataset achieves superior classification performance, demonstrating its effectiveness and the feasibility for neural networks to learn global point cloud data from components.