With the continuous development of three-dimensional (3D) data acquisition technology in recent years,the acquisition of three-dimensional large-scale scene point cloud data is becoming more and more convenient.At present,the deep learning network framework is becoming more and more mature in the field of two-dimensional (2D) image processing,while the large-scale scene point cloud is a kind of 3D irregular data.When using 3D convolutional neural networks in deep learning to directly process large-scale scene 3D data,there will be problems such as low classification accuracy and high computational complexity.Therefore,to effectively solve the problems of long computation time and low classification accuracy in point cloud classification based on deep learning,this paper proposes a binary neural network-based classification method for large-scale scene point cloud.designing the eigenvalue calculation method for irregular 3D point cloud data,processing the input point cloud feature images based on IR-Net binary neural network,further adopting Dynamic ReLU activation function to improve the computational efficiency of the neural network,and finally deriving the point cloud classification results.The experimental results show that the proposed method achieves 97.6% classification accuracy on the Oakland dataset and 92.3% and 97.2% in the GML dataset,Experimental results show that Dy-ResNet can effectively improve the accuracy of point cloud classification,reduce the complexity of calculation and improve training efficiency.