Aiming at the problems of low accuracy and poor efficiency of scene reconstruction caused by the defects of three-dimensional point cloud data acquisition of substation equipment, based on the analysis of the identification process, this paper proposes a classification and identification method of substation equipment combining K-nearest neighbor classification algorithm and improved particle swarm optimization algorithm. The improved particle swarm optimization algorithm is used to optimize the input weight of the K-nearest neighbor classifier and improve the classification and recognition accuracy of the equipment. The superiority of this method is verified by simulation and comparison analysis. The results show that the classification recognition effect of the proposed method is remarkable, the training accuracy rate is 100%, and the test accuracy rate is 99%. Compared with the traditional recognition method, the recognition accuracy rate is improved from 97% to 99%, and the average recognition time is reduced from 85.81s to 0.19s. This method solves the problems of low scene reconstruction accuracy, poor efficiency and low recognition rate caused by the defect of three-dimensional point cloud data acquisition of substation equipment, effectively improves the classification and recognition effect of substation equipment, which has good practical value and operability.