Traditionally, material surface classification has relied on reflectance spectrum measurement and pixel-wise comparison. For best results, the measurement usually requires measurement of the full spectral reflectance which can be the time-consuming issue and error-prone. Our previous works have proved that convolutional neural networks could learn the Bi-directional Texture Function (BTF) data feature hierarchy from pixels to the classifier. In this paper, we trained a wide-dense neural network for BTF data. The dense neural network was developed from the residual network (ResNet), which made a residual mapping for these stacked layers instead of directly fitting a desired underlying mapping. This shortcut idea has excellent performance in deeper layers. The dense structure reduced the redundancy within the feature maps of the individual layers and increased training speed. The narrow size of the dense net layers simplified the feature map for the whole network. The wide structure of the network was applied due to the resolution of our BTF data. In this paper, we generated BTF angular maps for material classification. Our data consisted of 151 lighting and 151 viewing directions. This angular resolution is higher than input data of conventional pre-trained dense network model. By adding a wide net structure, training time is reduced and classification performance improved. Training our own networks for specific data requires a large training dataset, therefore we augmented angular BTF image to improve the robustness of the training model. Within the over 30,000 BTF angular maps, we obtained a more reliable training model. Finally, we compared the improvement of our wide-dense network with other pre-trained neural networks and other feature extraction network.