Aiming at the image feature of blurred edge caused by halo,the spot target area is obtained by Niblack local threshold segmentation,and then the geometric features of spot image are extracted;using image edges segmented by Niblack to clip original spot image,and grayscale image of spot target that have been removed from the halo is obtained,on this basis,the spot area brightness is extract,and the 6-dimensional feature matrix combined with the geometric characteristics of spot is constructed.The ablation power is identified by BP neural network,Linear locally tangent space arrangement(LLTSA-BP) and Local preserving projection(LPP-BP) models respectively;further,ablation power is identified based on characteristic matrix after dimension reduction by ELM(Extreme Learning Machine),Linear locally tangent space arrangement(LLTSA-ELM) and Local preserving projection(LPP-ELM) dimensionality reduction models respectively.Through comparative studies,we found that convergence time classifying 6-dimensional feature matrices of BP neural network is shorter than that of ELM classification model,and the number of required hidden layer neurons is less.However,kinds of manifold learning-ELM models,be used to classify data after dimensionality reduction,performs better,which takes much less time than the BP neural network do,and among them,the LPP-ELM model has the best classification effect.