This paper addresses the problem of feature extraction for 3d point cloud data by using autoencoder. Deep learning is one of the most active fields of artificial intelligence, especially in a variety of visual applications, such as image classification and object recognition. However it has not been successfully applied on 3d point cloud data. In this paper, a new method of analyzing the point cloud data is proposed. The method aims to convert the point cloud data to a surface-condition-feature map, which is very effective and useful in pre-training by autoencoder. The surface-condition-features in this paper are defined as upward inclined, downward inclined, upward curved, downward curved, edge and flat, where those features are converted from surface normal vectors.