The exponential increasing of data being collected in astronomy has raised a big data challenge. Mining valuable information timely and efficiently from massive raw data is highly demanded. Even simple binary classification of collected raw data is of great importance, reducing the burden of the following data processing. Inspired by the success of image classification with deep learning, we investigated solar radio spectrum classification using deep learning, including the premier deep belief network (DBN), the most popular convolutional neural network (CNN) and long short-term memory (LSTM) network. For model training, a database of solar spectrum was established and published to the public. As far as we know, it is the first one in the world. The database contains 8816 spectrums with different image patterns which represent different solar radio mechanisms. Then, each spectrum was given a label by the invited experts of solar radio astronomy.