Compressive spectral imaging systems have promising applications in the field of object classification. However, for soil classification problem, conventional methods addressing this specific task often fail to produce satisfying results due to the tradeoff between the invariance and discrepancy of each soil. In this paper, we explore a liquid crystal tunable filters (LCTF)-based system and propose a three-dimensional convolutional neural network (3D-CNN) for soil classification. We first obtain a set of soil compressive measurements via a low spatial resolution detector, and soil hyperspectral images are reconstructed with improved resolution in spatial as well as spectral domains by a compressive sensing (CS) method. Furthermore, different from previous spectral-based object classification methods restricted to extract features from each type independently, on account of the potential of spectral property on individual solid, our method proposes to apply the principal component analysis(PCA) to achieve a dimensionality reduction in the spectral domain. Then, we explore a differential perception model for flexible feature extraction, and finally introduce a 3D-CNN framework to solve the multi-soil classification problem. Experimental results demonstrate that our algorithm not only is able to accelerate the ability of feature discriminability but also performs against conventional soil classification methods.