The spectrum is a comprehensive reflection of the mineral’s physical chemistry characteristics, composition and structure, which has been used in mineral and rock identification. The traditional classification methods of the mineral spectrum require complex spectral pretreatment, and then some spectral features are analyzed by different methods to achieve the goal of fine classification. However, the pretreatment may cause a partial loss of the spectral information and reduce the classification accuracy. Besides, the operation process is complex, so the efficiency is low, making it difficult to cope with the growing demand for big data processing. Therefore, it is important to establish an accurate, efficient and automatic classification model for the mineral spectrum. As one of the widely used deep learning models, the convolutional neural network extracts data features layer by layer and combines them to form higher-level semantic information. It has a strong capability of model formulation and great potential for the analysis of spectral data. This paper proposes a novel mineral spectrum classification method based on a one-dimensional dilated convolutional neural network (1D-DCNN). The DCNN is used for spectral feature extraction. The backpropagation algorithm combined with the random gradient descent optimizer is used to adjust the model’s parameters, then output the classification result, which implements the end-to-end discrimination of mineral species. The 1D-DCNN includes one input layer, three dilated convolution layers, two pooling layers, two full connection layers and one output layer. It uses cross-entropy as the loss function, and dilated convolution is introduced to enlarge the receptive field of filters effectively avoid the loss of spectral feature details. The spectrum of four different minerals, muscovite, dolomite, calcite and kaolinite, are collected, and the data are augmented by way of adding noise to construct sufficient spectral samples, which are used for model training and testing. Then, we explore the impacts of different model parameters, such as the convolution type and the number of iterations, and then compare the proposed model with the traditional mineral spectrum classification methods to evaluate its performance. Experimental results indicate that the 1D-DCNN model can quickly and accurately classify mineral spectrum with the accuracy of 99.32%, which is superior to the backpropagation (BP) algorithm and support vector machine (SVM) methods, and it shows that the proposed method can fully learn mineral spectral features and implement a fine classification result, with good robustness and scalability. The proposed method can apply further to the spectra classification in coal, oil-gas, lunar soil and other fields.