This study focuses on the optimization and application of key technologies for 3D point cloud deep learning. Aiming at the sparsity, disorder and noise interference of point cloud data, this study systematically studies the feature extraction method, model structure and training strategy, as well as large-scale data processing and real-time performance. Through multi-scale feature aggregation, graph convolution and attention mechanism and other technical means, the modeling ability of local geometric details and global semantic information is enhanced. Combining residual connection, adaptive optimization and mixed precision training, the convergence efficiency and overall robustness of the network are effectively improved, and the computational burden under high-dimensional input is reduced. In the application of autonomous driving scenarios, the new method optimizes key challenges such as long-distance target detection and small target recognition. The experimental results show that it achieves high accuracy and stability in multiple indicators, and can maintain reliable recognition performance in the case of severe occlusion or more noise, providing technical support for three-dimensional perception and online decision-making in intelligent transportation and other industrial fields.