This paper focuses on the deep learning feature extraction and analysis of 3D models and studies the application and performance of various deep learning-based feature extraction methods in 3D point cloud data processing. By designing and implementing models such as PointNet, PointNet++ and DGCNN, the ability of the models in capturing local and global features is explored, and the advantages of deep learning technology in improving classification accuracy and feature robustness are verified by experiments. The study also systematically optimizes and analyzes the hyperparameters of the model, uses 3D visualization technology to reveal the dynamic effects of factors such as learning rate and batch size on model performance and proposes an efficient network design strategy suitable for 3D feature extraction tasks. The results show that deep learning methods can not only effectively improve the accuracy of 3D feature extraction, but also provide technical support for application scenarios such as classification, retrieval and generation. This study provides new ideas for the optimization of 3D model feature extraction and lays a theoretical foundation for the realization of intelligent 3D tasks in practical scenarios.