At present, many domestic aero-engines are bought from abroad. Only physical objects and installation dimensions are provided for such aero-engines, and the lack of three-dimensional digital models brings great difficulties to the assembly coordination design of aircraft and aero-engines. Therefore, aircraft design departments urgently need to quickly reconstruct geometric models of aero-engine profiles. To enable a reconstructed geometric model of the aero-engine profile to retain exact structural features, this paper proposes a feature segmentation method of the aero-engine profile point clouds based on deep learning. It divides the whole point clouds into feature data and non-feature data, which is conducive to the subsequent reconstruction of various complex structural features by different methods. An iterative density equalization algorithm designed to create a feature segmentation dataset provides a basis for the training, testing, and performance evaluation of the feature segmentation network. A feature segmentation network is designed to collect the shape structure and local neighborhood information from multi-scale patches and thereby determine whether the center is a feature point. The trained feature segmentation network model is then applied to the profile point cloud of an aero-engine. The verification results show that the accuracy of feature segmentation reaches 95.16%, which means the proposed algorithm achieves high-precision semantic segmentation.