In order to mine and sense the geometric features of point clouds and further improve the semantic segmentation effect of point clouds by feature enhancement， a point clouds semantic segmentation network based on feature enhancement was proposed. Firstly， the Geometric Feature Sensing Of Point cloud （GFSOP） module was designed to make the network capable of sensing the local geometric structure of point clouds， semantic representations were enhanced by capturing spatial features between points， and multi-scale features were obtained by the idea of hierarchical extraction of features. At the same time， spatial attention and channel attention were fuseed to predict semantic labels of point clouds， and the segmentation performance was improved by strengthening spatial correlation and channel dependence. Experimental results on the indoor dataset S3DIS （Stanford large-scale 3D Indoor Spaces） show that compared with PointNet++， the proposed network improves the mean Intersection over Union （mIoU） by 5.7 percentage points and the Overall Accuracy （OA） by 3.1 percentage points， and has stronger generalization performance and more robust segmentation effect on point clouds with problems of noise， uneven point cloud density and unclear boundaries.

Aiming at the problem of spectrum allocation based on maximizing network benefit in cognitive radio and the fact that Manta Ray Foraging Optimization （MRFO） algorithm is difficult to solve the problem of spectrum allocation， a Discrete Manta Ray Foraging Optimization （DMRFO） algorithm was proposed.Considering the pro-1 characteristic of spectrum allocation problem in engineering， firstly， MRFO algorithm was discretely binarized based on the Sigmoid Function （SF） discrete method. Secondly， the XOR operator and velocity adjustment factor were used to guide the manta rays to adaptively adjust the position of next time to the optimal solution according to the current velocity. Then， the binary spiral foraging was carried out near the global optimal solution to avoid the algorithm from falling into the local optimum. Finally， the proposed DMRFO algorithm was applied to solve the spectrum allocation problem. Simulation results show that the convergence mean and standard deviation of the network benefit when using DMRFO algorithm to allocate spectrum are 362.60 and 4.14 respectively， which are significantly better than those of Discrete Artificial Bee Colony （DABC） algorithm， Binary Particle Swarm Optimization （BPSO） algorithm and Improved Binary Particle Swarm Optimization （IBPSO） algorithm.